A list of ICs and IPs for AI, Machine Learning and Deep Learning.

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AI Chip (ICs and IPs)

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IC VendorsIntel, Qualcomm, Nvidia, Samsung, AMD, Xilinx, IBM, STMicroelectronics, NXP, Marvell, MediaTek, HiSilicon, Rockchip, Renesas Electronics, Ambarella, Sony17
Tech Giants & HPC VendorsGoogle, Amazon_AWS, Microsoft, Apple, Aliyun, Alibaba Group, Tencent Cloud, Baidu, Baidu Cloud, HUAWEI, Fujitsu, Nokia, Facebook, HPE, Tesla, LG, SK Telecom16
IP VendorsARM, Synopsys, Imagination, CEVA, Cadence, VeriSilicon, Videantis7
Startups in China Cambricon, Horizon Robotics, Bitmain, Chipintelli, Thinkforce, Unisound, AISpeech, Rokid, NextVPU, Canaan, Enflame, Eesay Tech, WITINMEM, TSING MICRO, Black Sesame, Corerain16
Startups Worldwide Cerebras, Graphcore, PEZY, Tenstorrent, Blaize, Koniku, Adapteva, Knowm, Mythic, Kalray, BrainChip, AImotive, Leepmind, Krtkl, NovuMind, REM, TERADEEP, Deep Vision, Groq, Kneron, Esperanto Technologies, Gyrfalcon Technology, SambaNova Systems, GreenWaves Technology, Lightelligence, Lightmatter, ThinkSilicon, Innogrit, Kortiq, Hailo,Tachyum,AlphaICs,Syntiant, aiCTX, Flex Logix, Preferred Network, Cornami, Anaflash, Optaylsys, Eta Compute, Achronix, Areanna AI, Neuroblade, Luminous Computing, Efinix, AISTORM,,Untether AI, GrAI Matter Lab, Rain Neuromorphics, Applied Brain Research, XMOS, DinoPlusAI, Furiosa AI, Perceive, SimpleMachines, Neureality, Analog Inference, Quadric, EdgeQ, Innatera Nanosystems61

I. IC Vendors


NVIDIA Unveils Grace: A High-Performance Arm Server CPU For Use In Big AI Systems

Kicking off another busy Spring GPU Technology Conference for NVIDIA, this morning the graphics and accelerator designer is announcing that they are going to once again design their own Arm-based CPU/SoC. Dubbed Grace – after Grace Hopper, the computer programming pioneer and US Navy rear admiral – the CPU is NVIDIA’s latest stab at more fully vertically integrating their hardware stack by being able to offer a high-performance CPU alongside their regular GPU wares. According to NVIDIA, the chip is being designed specifically for large-scale neural network workloads, and is expected to become available in NVIDIA products in 2023.

NVIDIA Ampere Architecture In-Depth

Today, during the 2020 NVIDIA GTC keynote address, NVIDIA founder and CEO Jensen Huang introduced the new NVIDIA A100 GPU based on the new NVIDIA Ampere GPU architecture. This post gives you a look inside the new A100 GPU, and describes important new features of NVIDIA Ampere architecture GPUs.

NVDLA Deep Learning Inference Compiler is Now Open Source

With the open-source release of NVDLA’s optimizing compiler on GitHub, system architects and software teams now have a starting point with the complete source for the world’s first fully open software and hardware inference platform.


Powering the TensorRT Hyperscale Inference Platform.

NVIDIA Reveals Next-Gen Turing GPU Architecture: NVIDIA Doubles-Down on Ray Tracing, GDDR6, & More

at NVIDIA’s SIGGRAPH 2018 keynote presentation, company CEO Jensen Huang formally unveiled the company’s much awaited (and much rumored) Turing GPU architecture. The next generation of NVIDIA’s GPU designs, Turing will be incorporating a number of new features and is rolling out this year.


On edge, Nvidia provide NVIDIA DRIVE™ PX, The AI Car Computer for Autonomous Driving and JETSON TX1/TX2 MODULE, “The embedded platform for autonomous everything”.


Nvidia anouced “XAVIER DLA NOW OPEN SOURCE” on GTC2017. We did not see Early Access verion yet. Hopefully, the general release will be avaliable on Sep. as promised. For more analysis, you may want to read 从Nvidia开源深度学习加速器说起.

Now the open source DLA is available on Github and more information can be found here.

The NVIDIA Deep Learning Accelerator (NVDLA) is a free and open architecture that promotes a standard way to design deep learning inference accelerators. With its modular architecture, NVDLA is scalable, highly configurable, and designed to simplify integration and portability. The hardware supports a wide range of IoT devices. Delivered as an open source project under the NVIDIA Open NVDLA License, all of the software, hardware, and documentation will be available on GitHub. Contributions are welcome.


Intel® Nervana™ Neural Network processors

Mobileye EyeQ

Mobileye is currently developing its fifth generation SoC, the EyeQ®5, to act as the vision central computer performing sensor fusion for Fully Autonomous Driving (Level 5) vehicles that will hit the road in 2020. To meet power consumption and performance targets, EyeQ® SoCs are designed in most advanced VLSI process technology nodes – down to 7nm FinFET in the 5th generation.


New Intel Vision Accelerator Solutions Speed Deep Learning and Artificial Intelligence on Edge Devices

Today, Intel unveiled its family of Intel® Vision Accelerator Design Products targeted at artificial intelligence (AI) inference and analytics performance on edge devices, where data originates and is acted upon. The new acceleration solutions come in two forms: one that features an array of Intel® Movidius™ vision processors and one built on the high-performance Intel® Arria® 10 FPGA.


Intel FPGA OpenCL and Solutions.


Intel Advances Neuromorphic with Loihi 2, New Lava Software Framework and New Partners

Second-generation research chip uses pre-production Intel 4 process, grows to 1 million neurons. Intel adds open software framework to accelerate developer innovation and path to commercialization.


Habana Gaudi debuts in the Amazon EC2 cloud

The primary motivation to create this new training instance class was presented by Andy Jassy in the 2020 re:Invent: “To provide our end-customers with up to 40% better price-performance than the current generation of GPU-based instances.”

Qualcomm Launches Cloud AI Chip

Last year, Qualcomm teased its Cloud AI100, promising strong performance and power efficiency to enable Artificial Intelligence in cloud edge computing, autonomous vehicles and 5G infrastructure. Today, the company announced it is now sampling the platform, with volume shipments planned for the first half of 2021. This begs the question: why would a company known for low-power cell-phone chips and IP decide to enter the data center market, which is full of players who have been there for decades?

Qualcomm Brings Power Efficient Artificial Intelligence Inference Processing to the Cloud

Qualcomm Technologies, Inc., a subsidiary of Qualcomm Incorporated (NASDAQ: QCOM), announced that it is bringing the Company’s artificial intelligence (AI) expertise to the cloud with the Qualcomm® Cloud AI 100. Built from the ground up to meet the explosive demand for AI inference processing in the cloud, the Qualcomm Cloud AI 100 utilizes the Company’s heritage in advanced signal processing and power efficiency.

Snapdragon 855 Mobile Platform

Our 4th generation on-device AI engine is the ultimate personal assistant for camera, voice, XR and gaming – delivering smarter, faster and more secure experiences. Utilizing all cores, it packs 3 times the power of its predecessor for stellar on-device AI capabilities... Greater than 7 trillion operations per second (TOPS)

Samsung Brings On-device AI Processing for Premium Mobile Devices with Exynos 9 Series 9820 Processor

Fourth-generation custom core and 2.0Gbps LTE Advanced Pro modem enables enriched mobile experiences including AR and VR applications

Samsung resently unveiled “The new Exynos 9810 brings premium features with a 2.9GHz custom CPU, an industry-first 6CA LTE modem and deep learning processing capabilities”.

The soon to be released AMD Radeon Instinct MI25 is promising 12.3 TFlops of SP or 24.6 TFlops of FP16. If your calculations are amenable to Nvidia’s Tensors, then AMD can’t compete. Nvidia also does twice the bandwidth with 900GB/s versus AMD’s 484 GB/s.

AMD has put a very good X86 server processor into the market for the first time in nine years, and it also has a matching GPU that gives its OEM and ODM partners a credible alternative for HPC and AI workload to the combination of Intel Xeons and Nvidia Teslas that dominate hybrid computing these days.

Tesla is reportedly developing its own processor for artificial intelligence, intended for use with its self-driving systems, in partnership with AMD. Tesla has an existing relationship with Nvidia, whose GPUs power its Autopilot system, but this new in-house chip reported by CNBC could potentially reduce its reliance on third-party AI processing hardware.

Xilinx Launches the World’s Fastest Data Center and AI Accelerator Cards

Xilinx launched Alveo, a portfolio of powerful accelerator cards designed to dramatically increase performance in industry-standard servers across cloud and on-premise data centers.

Xilinx provide “Machine Learning Inference Solutions from Edge to Cloud” and naturally claim their FPGA’s are best for INT8 with one of their white papers.

Whilst performance per Watt is impressive for FPGAs, the vendors’ larger chips have long had earth shatteringly high chip prices for the larger chips. Finding a balance between price and capability is the main challenge with the FPGAs.

TrueNorth is IBM's Neuromorphic CMOS ASIC developed in conjunction with the DARPA SyNAPSE program.

It is a manycore processor network on a chip design, with 4096 cores, each one simulating 256 programmable silicon "neurons" for a total of just over a million neurons. In turn, each neuron has 256 programmable "synapses" that convey the signals between them. Hence, the total number of programmable synapses is just over 268 million (228). In terms of basic building blocks, its transistor count is 5.4 billion. Since memory, computation, and communication are handled in each of the 4096 neurosynaptic cores, TrueNorth circumvents the von-Neumann-architecture bottlenecks and is very energy-efficient, consuming 70 milliwatts, about 1/10,000th the power density of conventional microprocessors. Wikipedia

With IBM POWER9, we’re all riding the AI wave

"With POWER9, we’re moving to a new off-chip era, with advanced accelerators like GPUs and FPGAs driving modern workloads, including AI...POWER9 will be the first commercial platform loaded with on-chip support for NVIDIA’s next-generation NVLink, OpenCAPI 3.0 and PCI-Express 4.0. These technologies provide a giant hose to transfer data."

AI Hardware Center

"The IBM Research AI Hardware Center is a global research hub headquartered in Albany, New York. The center is focused on enabling next-generation chips and systems that support the tremendous processing power and unprecedented speed that AI requires to realize its full potential.

ST preps second neural network IC

STMicroelectronics is designing a second iteration of the neural networking technology that the company reported on at the International Solid-State Circuits Conference (ISSCC) in February 2017.

ISSCC2017 Deep-Learning Processors文章学习 (一) is a reference.


The NXP S32 automotive platform is the world’s first scalable automotive computing architecture. It offers a unified hardware platform and an identical software environment across application domains to bring rich in-vehicle experiences and automated driving functions to market faster.

S32V234: Vision Processor for Front and Surround View Camera, Machine Learning and Sensor Fusion Applications

The S32V234 is our 2nd generation vision processor family designed to support computation intensive applications for image processing and offers an ISP, powerful 3D GPU, dual APEX-2 vision accelerators, security and supports SafeAssure™. S32V234 is suited for ADAS, NCAP front camera, object detection and recognition, surround view, machine learning and sensor fusion applications. S32V234 is engineered for automotive-grade reliability, functional safety and security measures to support vehicle and industrial automation.

Marvell Demonstrates Artificial Intelligence SSD Controller Architecture Solution

Marvell will demonstrate today at the Flash Memory Summit how it will provide artificial intelligence capabilities to a broad range of industries by incorporating NVIDIA’s Deep Learning Accelerator (NVDLA) technology in its family of data center and client SSD controllers.

MediaTek announced Helio P90, highlighting AI processing.

This article, "MediaTek Announces New Premium Helio P90 SoC", from AnandTech has more in-deepth analysis.

Kirin for Smart Phone
Kirin 980, the World's First 7nm Process Mobile AI Chipset

Introducing the Kirin 980, the world's first 7nm process mobile phone SoC chipset, the world’s first cortex-A76 architecture chipset, the world’s first dual NPU design, and the world’s first chipset to support LTE Cat.21. The Kirin 980 combines multiple technological inFtions and leads the AI trend to provide users with impressive mobile performance and to create a more convenient and intelligent life.

HiSilicon Kirin 970 Processor annouced fearturing with dedicated Neural-network Processing Unit.
In this article,we can find more details about NPU in Kirin970.

Mobile Camera SoC
According to a Brief Data Sheet of Hi3559A V100ESultra-HD Mobile Camera SoC, it has:

Dual-core CNN@700 MHz neural network acceleration engine

Rockchip Released Its First AI Processor RK3399Pro -- NPU Performance up to 2.4TOPs

RK3399Pro adopted exclusive AI hardware design. Its NPU computing performance reaches 2.4TOPs, and indexes of both high performance and low consumption keep ahead: the performance is 150% higher than other same type NPU processor; the power consumption is less than 1%, comparing with other solutions adopting GPU as AI computing unit.

Renesas Electronics Develops New Processing-In-Memory Technology for Next-Generation AI Chips that Achieves AI Processing Performance of 8.8 TOPS/W

Renesas Electronics Corporation (TSE: 6723), a premier supplier of advanced semiconductor solutions, today announced it has developed an AI accelerator that performs CNN (convolutional neural network) processing at high speeds and low power to move towards the next generation of Renesas embedded AI (e-AI), which will accelerate increased intelligence of endpoint devices. A Renesas test chip featuring this accelerator has achieved the power efficiency of 8.8 TOPS/W (Note 1), which is the industry's highest class of power efficiency. The Renesas accelerator is based on the processing-in-memory (PIM) architecture, an increasingly popular approach for AI technology, in which multiply-and-accumulate operations are performed in the memory circuit as data is read out from that memory.

Intelligent Vision Processors For Edge Applications

Sony to Release World's First Intelligent Vision Sensors with AI Processing Functionality

Synaptics Expands into Low Power Edge AI Applications with New Katana Platform

SAN JOSE, Calif., Dec. 15, 2020 – Synaptics® Incorporated (Nasdaq: SYNA), today announced the Katana Edge AI™ platform, addressing a growing industry gap for solutions that enable battery powered devices for consumer and industrial IoT markets. The platform combines Synaptics’ proven low power SoC architecture with energy-efficient AI software, enabled by a partnership with Eta Compute. The Katana solution is optimized for a wide range of ultra-low power use cases in edge devices for office buildings, retail, factories, farms and smart homes. Typical applications include people or object recognition and counting, visual, voice or sound detection, asset or inventory tracking and environmental sensing.

II. Tech Giants & HPC Vendors

Google Tensor: Everything you need to know about the Pixel 6 chip

Google has taken the wraps off its latest Pixel smartphones and, among the changes, the one with the biggest long-term impact is the switch to in-house silicon for the search giant.

Google Launches TPU v4 AI Chips

Google CEO Sundar Pichai spoke for only one minute and 42 seconds about the company’s latest TPU v4 Tensor Processing Units during his keynote at the Google I/O virtual conference this week, but it may have been the most important and awaited news from the event.

Google begins selling the $150 Coral Dev Board, a hardware kit for accelerated AI edge computing

If you’re a software dev looking to get a head start on AI development at the edge, why not try on Google’s new hardware for size? The search company today made available the Coral Dev Board, a $150 computer featuring a removable system-on-module with one of its custom tensor processing unit (TPU) AI chips.

Google's original TPU had a big lead over GPUs and helped power DeepMind's AlphaGo victory over Lee Sedol in a Go tournament. The original 700MHz TPU is described as having 95 TFlops for 8-bit calculations or 23 TFlops for 16-bit whilst drawing only 40W. This was much faster than GPUs on release but is now slower than Nvidia's V100, but not on a per W basis. The new TPU2 is referred to as a TPU device with four chips and can do around 180 TFlops. Each chip's performance has been doubled to 45 TFlops for 16-bits. You can see the gap to Nvidia's V100 is closing. You can't buy a TPU or TPU2.

Lately, Google is making Cloud TPUs available for use in Google Cloud Platform (GCP). Here you can find the latest banchmark result of Google TPU2.

Pixel Visual Core is Google’s first custom-designed co-processor for consumer products. It’s built into every Pixel 2, and in the coming months, we’ll turn it on through a software update to enable more applications to use Pixel 2’s camera for taking HDR+ quality pictures.

Tearing Apart Google’s TPU 3.0 AI Coprocessor

Google did its best to impress this week at its annual IO conference. While Google rolled out a bunch of benchmarks that were run on its current Cloud TPU instances, based on TPUv2 chips, the company divulged a few skimpy details about its next generation TPU chip and its systems architecture. The company changed from version notation (TPUv2) to revision notation (TPU 3.0) with the update, but ironically the detail we have assembled shows that the step from TPUv2 to what we will call TPUv3 probably isn’t that big; it should probably be called TPU v2r5 or something like that.

Edge TPU

AI is pervasive today, from consumer to enterprise applications. With the explosive growth of connected devices, combined with a demand for privacy/confidentiality, low latency and bandwidth constraints, AI models trained in the cloud increasingly need to be run at the edge. Edge TPU is Google’s purpose-built ASIC designed to run AI at the edge. It delivers high performance in a small physical and power footprint, enabling the deployment of high-accuracy AI at the edge.

Other references are:
Google TPU3 看点

Google TPU 揭密


脉动阵列 - 因Google TPU获得新生

Should We All Embrace Systolic Arrays?

Amazon may be developing AI chips for Alexa

The Information has a report this morning that Amazon is working on building AI chips for the Echo, which would allow Alexa to more quickly parse information and get those answers.

AWS launches Trainium, its new custom ML training chip

AAt its annual re:Invent developer conference, AWS today announced the launch of AWS Trainium, the company’s next-gen custom chip dedicated to training machine learning models. The company promises that it can offer higher performance than any of its competitors in the cloud, with support for TensorFlow, PyTorch and MXNet.

AWS Inferentia. High performance machine learning inference chip, custom designed by AWS.

AWS Inferentia provides high throughput, low latency inference performance at an extremely low cost. Each chip provides hundreds of TOPS (tera operations per second) of inference throughput to allow complex models to make fast predictions. For even more performance, multiple AWS Inferentia chips can be used together to drive thousands of TOPS of throughput. AWS Inferentia will be available for use with Amazon SageMaker, Amazon EC2, and Amazon Elastic Inference.

AWS FPGA instance

Amazon EC2 F1 is a compute instance with field programmable gate arrays (FPGAs) that you can program to create custom hardware accelerations for your application. F1 instances are easy to program and come with everything you need to develop, simulate, debug, and compile your hardware acceleration code, including an FPGA Developer AMI and Hardware Developer Kit (HDK). Once your FPGA design is complete, you can register it as an Amazon FPGA Image (AFI), and deploy it to your F1 instance in just a few clicks. You can reuse your AFIs as many times, and across as many F1 instances as you like.

Inside the Microsoft FPGA-based configurable cloud is also a good reference if want to know Microsoft's vision on FPGA in cloud.

This article "智慧云中的FPGA" gives and overview about FPGA used in AI aceleration in the cloud.

Drilling Into Microsoft’s BrainWave Soft Deep Learning Chip shows more details based on Microsoft's presentation on Hot Chips 2017.

Real-time AI: Microsoft announces preview of Project Brainwave

At Microsoft’s Build developers conference in Seattle this week, the company is announcing a preview of Project Brainwave integrated with Azure Machine Learning, which the company says will make Azure the most efficient cloud computing platform for AI.

Microsoft is hiring engineers to work on A.I. chip design for its cloud

Microsoft is following Google's lead in designing a computer processor for artificial intelligence, according to recent job postings.

A12 Bionic The smartest, most powerful chip in a smartphone.

A whole new level of intelligence. The A12 Bionic, with our next-generation Neural Engine, delivers incredible performance. It uses real-time machine learning to transform the way you experience photos, gaming, augmented reality, and more.

Apple unveiled the new processor powering the new iPhone 8 and iPhone X - the A11 Bionic. The A11 also includes dedicated neural network hardware that Apple calls a "neural engine", which can perform up to 600 billion operations per second.
Core ML is Apple's current sulotion for machine learning application.

Alibaba’s New AI Chip Can Process Nearly 80K Images Per Second

At the Alibaba Cloud (Aliyun) Apsara Conference 2019, Pingtouge unveiled its first AI dedicated processor for cloud-based large-scale AI inferencing. The Hanguang 800 is the first semiconductor product in Alibaba’s 20-year history.

Tencent cloud introduces FPGA instance(Beta), with three different specifications based on Xilinx Kintex UltraScale KU115 FPGA. They will provide more choices equiped with Inter FPGA in the future.

Baidu A.I. chip unit valued at $2 billion after funding and may become standalone business

Baidu has raised money for its artificial intelligence (AI) semiconductor business at a valuation of $2 billion.The funding round was led by CPE, a Chinese asset management and private equity firm.Now that the Kunlun chip business has raised money, it could pave the way for the unit to be spun-off, but no final decision has been made.

Chinese tech giant Huawei unveils A.I. chips, taking aim at giants like Qualcomm and Nvidia

Huawei unveils two new artificial intelligence (AI) chips called the Ascend 910 and Ascend 310. The two chips are aimed at uses in data centers and internet-connected consumer devices, Rotating Chairman Eric Xu says at the Huawei Connect conference in Shanghai. The move pits the Chinese tech giant against major chipmakers including Qualcomm and Nvidia.

FPGA Accelerated Cloud Server, high performance FPGA instance is open for beta test.

FPGA云服务器提供CPU和FPGA直接的高达100Gbps PCIe互连通道,每节点提供8片Xilinx VU9P FPGA,同时提供FPGA之间高达200Gbps的Mesh光互连专用通道,让您的应用加速需求不再受到硬件限制。

This DLU that Fujitsu is creating is done from scratch, and it is not based on either the Sparc or ARM instruction set and, in fact, it has its own instruction set and a new data format specifically for deep learning, which were created from scratch. Japanese computing giant Fujitsu. Which knows a thing or two about making a very efficient and highly scalable system for HPC workloads, as evidenced by the K supercomputer, does not believe that the HPC and AI architectures will converge. Rather, the company is banking on the fact that these architectures will diverge and will require very specialized functions.

Nokia has developed the ReefShark chipsets for its 5G network solutions. AI is implemented in the ReefShark design for radio and embedded in the baseband to use augmented deep learning to trigger smart, rapid actions by the autonomous, cognitive network, enhancing network optimization and increasing business opportunities.

Facebook Is Forming a Team to Design Its Own Chips

Facebook Inc. is building a team to design its own semiconductors, adding to a trend among technology companies to supply themselves and lower their dependence on chipmakers such as Intel Corp. and Qualcomm Inc., according to job listings and people familiar with the matter.


In the context of a broader discussion about the company’s Extreme Edge program focused on space-bound systems, HPE’s Dr. Tom Bradicich, VP and GM of Servers, Converged Edge, and IoT systems, described a future chip that would be ideally suited for high performance computing under intense power and physical space limitations characteristic of space missions. To be more clear, he told us as much as he could—very little is known about the architecture, but there was some key elements he described.

Tesla Dojo – Unique Packaging and Chip Design Allow An Order Magnitude Advantage Over Competing AI Hardware

Tesla hosted their AI Day and revealed the innerworkings of their software and hardware infrastructure. Part of this reveal was the previously teased Dojo AI training chip. Tesla claims their D1 Dojo chip has a GPU level compute, CPU level flexibility, with networking switch IO.

Tesla’s new AI chip isn’t a silver bullet for self-driving cars

Processing power is important, but building chips could be an expensive distraction for Tesla


New AI Processor with LG Neural Engine Designed for Use in Various Products Including Robot Vacuum Cleaners, Washing Machines and Refrigerators

SKT Unveils its AI Chip and New Plans for AI Semiconductor Business

Nov. 25, 2020 — SK Telecom (SKT) today unveiled its self-developed artificial intelligence (AI) chip named ‘SAPEON X220’ and shared its AI semiconductor business vision.

III. Traditional IP Vendors

DynamIQ is embedded IP giant's answer to AI age. It may not be a revolutionary design but is important for sure.

ARM also provide a open source Compute Library contains a comprehensive collection of software functions implemented for the Arm Cortex-A family of CPU processors and the Arm Mali family of GPUs.

Arm Machine Learning Processor

Specifically designed for inference at the edge, the ML processor gives an industry-leading performance of 4.6 TOPs, with a stunning efficiency of 3 TOPs/W for mobile devices and smart IP cameras.

ARM Details "Project Trillium" Machine Learning Processor Architecture

Arm details more of the architecture of what Arm now seems to more consistently call their “machine learning processor” or MLP from here on now. The MLP IP started off a blank sheet in terms of architecture implementation and the team consists of engineers pulled off from the CPU and GPU teams.

DesignWare EV6x Embedded Vision Processors

处理器IP厂商的机器学习方案 - Synopsys

PowerVR Series2NX Neural Network Accelerator

Imagination launches multi-core IMG Series4 NNA – the ultimate AI accelerator delivering industry-disruptive performance for ADAS and autonomous driving

the company is announcing the first products in the 2NX NNA family: the higher-performance AX2185 and lower-cost AX2145.

CEVA-XM6 Fifth-generation computer vision and deep learning embedded platform

处理器IP厂商的机器学习方案 - CEVA

CEVA Announces NeuPro Neural Network IP

Ahead of CES CEVA announced a new specialised neural network accelerator IP called NeuPro.

Tensilica Vision DSPs for Imaging, Computer Vision, and Neural Networks

VeriSilicon’s Vivante VIP8000 Neural Network Processor IP Delivers Over 3 Tera MACs Per Second


The v-MP6000UDX processor from Videantis is a scalable processor family that has been designed to run high-performance deep learning, computer vision, imaging and video coding applications in a low power footprint.

IV. Startups in China

Chinese AI Chip Maker Cambricon Unveils New Cloud-Based Smart Chip

Chinese artificial intelligence chip maker Cambricon Technologies Corp Ltd has unveiled two new products, a cloud-based smart chip Cambricon MLU100 and a new version of its AI processor IP product Cambricon 1M, at a launching event in Shanghai on May 3rd.

Cambricon release new product page, including IP, Chip and Software tools

AI Chip Explosion: Cambricon’s Billion-Device Ambition

On November 6 in Beijing, China’s rising semiconductor company Cambricon released the Cambrian-1H8 for low power consumption computer vision application, the higher-end Cambrian-1H16 for more general purpose application, the Cambrian-1M for autonomous driving applications with yet-to-be-disclosed release date, and an AI system software named Cambrian NeuWare.

Chinese AI chip maker Horizon Robotics raises $600 million from SK Hynix, others

Chinese chip maker Horizon Robotics said on Wednesday it had raised $600 million in its latest funding round, bringing its valuation to $3 billion, amid a push from Chinese companies and the government to boost the semiconductor industry.

Dec. 20, Horizon Robotics annouced two chip products, "Journey" for ADAS and "Sunrise" for Smart Cameras.

Bitcoin Mining Giant Bitmain is developing processors for both training and inference tasks.

Bitmain’s newest product, the Sophon, may or may not take over deep learning. But by giving it such a name Zhan and his Bitmain co-founder, Jihan Wu, have signaled to the world their intentions. The Sophon unit will include Bitmain’s first piece of bespoke silicon for a revolutionary AI technology. If things go to plan, thousands of Bitmain Sophon units soon could be training neural networks in vast data centers around the world.

On Nov.8, Bitmain announced its Sophon BM1869 Tensor Computing Processor, Deep Learning Accelerating Card SC1 and IVS server SS1.

Chipintelli's first IC, CI1006, is designed for automatic speech recognition application.

Sequoia, Hillhouse, Yitu Technology Join $68M Series A Round In Chinese AI Chip Maker ThinkForce

Unisound raises US$100 million to fund AI, chip development

China’s AISpeech Raises $76M on Advanced Speech Tech; Eyes AI Chips

Chinese AI startup Rokid will mass produce their own custom AI chip for voice recognition

The world leading computer vision processing IC and system company, NextVPU, today unveiled AI vision processing IC N171. N171 is the flagship IC of NextVPU’s N1 series computer vison chips. As a VPU, N171 pushes the Edge AI computing limit further from many aspects. With powerful computing engines embedded, N171 has unprecedent geometry calculation and deep neural network processing capabilities, and can be widely used in surveillance, robots, drones, UGV, smart home, ADAS applications, etc.

Canaan's Kendryte is a series of AI chips which focuses on IoT.

Enflame Tech is a startup company based in Shanghai, China. It was established in March 2018 with two R&D centers in Shanghai and Beijing. Enflame is developing the deep learning accelerator SoCs and software stack, targeting AI training platform solutions for the Cloud service provider and the data centers.

Enflame Technology Announces CloudBlazer with DTU Chip on GLOBALFOUNDRIES 12LP FinFET Platform for Data Center Training

SHANGHAI, China, Dec. 12, 2019 – In conjunction with the launch of Enflame’s CloudBlazer T10, Enflame Technology and GLOBALFOUNDRIES (GF) today announced a new high-performing deep learning accelerator solution for data center training. Designed to accelerate deep learning deployment, the accelerator’s core Deep Thinking Unit (DTU) is based on GF’s 12LP FinFET platform with 2.5D packaging to deliver fast, power-efficient data processing for cloud-based AI training platforms.

Chinese tech startups Cloudpick, EEasy Tech snag Intel Capital funding

EEasy Technology Co. Ltd is an AI system-on-chip (SoC) design house and total solution provider. Its offerings include AI acceleration; image and graphic processing; video encoding and decoding; and mixed-signal ULSI design capabilities.

Founded in Oct. 2017, WITINMEM focuses on Low cost, low power AI chips and system solutions based on processing-in-memory technology in NOR Flash memory.

Qingwei Intelligent Technology (Tsing Micro) is AI chip company spin-off from Tsinghua University.

Black Sesame Technologies Nearly Completes 100 Million Series B Financing Round

Black Sesame Technologies (黑芝麻智能科技) has nearly completed its 100 million Series B Financing round which will be used to expand cooperation with OEMs, accelerate mass production, reference design development of autopilot controllers, and software-vehicle integration.

V. Startups Worldwide

Cerebras Completes Series F Funding, Another $250M for $4B Valuation

The new Series F funding round nets the company another $250m in capital, bringing the total raised through venture capital up to $720 million.

Cerebras Unveils Wafer Scale Engine Two (WSE2): 2.6 Trillion Transistors, 100% Yield

Two years ago Cerebras unveiled a revolution in silicon design: a processor as big as your head, using as much area on a 12-inch wafer as a rectangular design would allow, built on 16nm, focused on both AI as well as HPC workloads. Today the company is launching its second generation product, built on TSMC 7nm, with more than double the cores and more than double of everything.

The Cerebras CS-1 computes deep learning AI problems by being bigger, bigger, and bigger than any other chip

Today, the company announced the launch of its end-user compute product, the Cerebras CS-1, and also announced its first customer of Argonne National Laboratory.

Wave Computing and MIPS Wave Goodbye

Word on the virtual street is that Wave Computing is closing down. The company has reportedly let all employees go and filed for Chapter 11. As one of the many promising new companies in the field of AI, Wave Computing was founded in 2008 with the mission “to revolutionize deep learning with real-time AI solutions that scale from the edge to the datacenter.”


Graphcore, the AI chipmaker, raises another $150M at a $1.95B valuation

Graphcore, the Bristol-based startup that designs processors specifically for artificial intelligence applications, announced it has raised another $150 million in funding for R&D and to continue bringing on new customers. It’s valuation is now $1.95 billion.

Microsoft and Graphcore Colleborate to Accelerate Artificial Intelligence

解密又一个xPU:Graphcore的IPU give some analysis on its IPU architecture.

Graphcore AI芯片:更多分析 More analysis.

深度剖析AI芯片初创公司Graphcore的IPU In-depth analysis after more information was disclosed.

The 2,048-core PEZY-SC2 sets a Green500 record

The SC2 is a second-generation chip featuring twice as many cores – i.e., 2,048 cores with 8-way SMT for a total of 16,384 threads. Operating at 1 GHz with 4 FLOPS per cycle per core as with the SC, the SC2 has a peak performance of 8.192 TFLOPS (single-precision). Both prior chips were manufactured on TSMC’s 28HPC+, however in order to enable the considerably higher core count within reasonable power consumption, PEZY decided to skip a generation and go directly to TSMC’s 16FF+ Technology.

Tenstorrent Raises over $200 million at $1 billion Valuation to Create Programmable, High Performance AI Computers

TORONTO, May 20, 2021 /PRNewswire/ - Tenstorrent, a hardware start-up developing next generation computers, announced today that it has raised over $200 million in a recent funding round that values the company at $1 billion. The round was led by Fidelity Management and Research Company and includes additional investments from Eclipse Ventures, Epic CG and Moore Capital.

Jim Keller Becomes CTO at Tenstorrent: "The Most Promising Architecture Out There

Today Tenstorrent is announcing that Jim Keller, compute architect extraordinaire, has joined the company as its Chief Technology Officer, President, and joins the company board.

Tenstorrent Launches AI Chip With Conditional Execution

Now Tenstorrent’s claim to future fame and potentially the crown is all about reducing the computation required to get to a good answer, instead of throwing massive amounts of brute-force compute at the problem. The technique is called fine-grained conditional computation, and it is just the beginning of an optimization roadmap Tenstorrent CEO, Ljubisa Bajic, has up his sleeves.

Tenstorrent is a small Canadian start-up in Toronto claiming an order of magnitude improvement in efficiency for deep learning, like most. No real public details but they're are on the Cognitive 300 list.

Blaize emerges from stealth with $87 million for its custom-designed AI chips

The fierce competition isn’t deterring Blaize (formerly Thinci), which hopes to stand out from the crowd with a novel graph streaming architecture. The nine-year-old startup’s claimed system-on-chip performance is impressive, to be fair, which is likely why it’s raised nearly $100 million from investors including automotive component maker Denso.

Founded in 2014, Newark, California startup Koniku has taken in $1.65 million in funding so far to become “the world’s first neurocomputation company“. The idea is that since the brain is the most powerful computer ever devised, why not reverse engineer it? Simple, right? Koniku is actually integrating biological neurons onto chips and has made enough progress that they claim to have AstraZeneca as a customer. Boeing has also signed on with a letter of intent to use the technology in chemical-detecting drones.

Adapteva has taken in $5.1 million in funding from investors that include mobile giant Ericsson. The paper "Epiphany-V: A 1024 processor 64-bit RISC System-On-Chip" describes the design of Adapteva's 1024-core processor chip in 16nm FinFet technology.

Knowm is actually setup as a .ORG but they appear to be pursuing a for-profit enterprise. The New Mexcio startup has taken in an undisclosed amount of seed funding so far to develop a new computational framework called AHaH Computing (Anti-Hebbian and Hebbian). The gory details can be found in this publication, but the short story is that this technology aims to reduce the size and power consumption of intelligent machine learning applications by up to 9 orders of magnitude.

The Era of Analog Compute has Arrived!

ResNet-50 in our prototype analog AI processor. Production release will support 900-1000 fps and INT8 accuracy at 3W.

A battery powered neural chip from Mythic with 50x lower power.

Founded in 2012, Texas-based startup Mythic (formerly known as Isocline) has taken in $9.5 million in funding with Draper Fisher Jurvetson as the lead investor. Prior to receiving any funding, the startup has taken in $2.5 million in grants. Mythic is developing an AI chip that “puts desktop GPU compute capabilities and deep neural networks onto a button-sized chip – with 50x higher battery life and far more data processing capabilities than competitors“. Essentially, that means you can give voice control and computer vision to any device locally without needing cloud connectivity.

Kalray Releases the Kalray Neural Network 3.0

Kalray (Euronext Growth Paris – ALKAL), a pioneer in processors for new intelligent systems, has announced the launch of the Kalray Neural Network 3.0 (KaNN), a platform for Artificial Intelligence application development. KaNN allows developers to seamlessly port their AI-based algorithms from well-known machine learning frameworks including Caffe, Torch and TensorFlow onto Kalray’s Massively Parallel Processor Array (MPPA) intelligent processor.

BrainChip Showcases Vision and Learning Capabilities of its Akida Neural Processing IP and Device at tinyML Summit 2020

BrainChip Holdings Ltd. (ASX: BRN), a leading provider of ultra-low power, high-performance edge AI technology, today announced that it will present its revolutionary new breed of neuromorphic processing IP and Device in two sessions at the tinyML Summit at the Samsung Strategy & Innovation Center in San Jose, California February 12-13.

BrainChip Inc (CA. USA) was the first company to offer a Spiking Neural processor, which was patented in 2008 (patent US 8,250,011). The current device, called the BrainChip Accelerator is a chip intended for rapid learning. It is offered as part of the BrainChip Studio software. BrainChip is a publicly listed company as part of BrainChip Holdings Ltd.

aiWare3 Hardware IP Helps Drive Autonomous Vehicles To Production.</p>

Latest technology enables scalable, low-power automotive inference engines with >50 TMAC/s NN processing power.

MOUNTAIN VIEW, Calif., October 30, 2018 – AImotive™, the global provider of full stack, vision-first self-driving technology, today announced the release of aiWare3™, the company’s 3rd generation, scalable, low-power, hardware Neural Network (NN) acceleration core.

Leepmind is carrying out research on original chip architectures in order to implement Neural Networks on a circuit enabling low power DeepLearning

A crowdfunding effort for Snickerdoodle raised $224,876 and they’re currenty shipping. If you pre-order one, they’ll deliver it by summer. The palm-sized unit uses the Zynq “System on Chip” (SoC) from Xilinix.

NovuMind combines big data, high-performance, and heterogeneous computing to change the Internet of Things (IoT) into the Intelligent Internet of Things (I²oT). Here is a paper from Moor Insights & Strategy, a global technology analyst and research firm. about NovuMind

Reduced Energy Microsystems are developing lower power asynchronous chips to suit CNN inference. REM was Y Combinator's first ASIC venture according to TechCrunch.

TeraDeep is building an AI Appliance using its deep learning FPGA’s acceleration. The company claims image recognition performance on AlexNet to achieve a 2X performance advantage compared with large GPUs, while consuming 5X less power. When compared to Intel’s Xeon processor, TeraDeep’s Accel technology delivers 10X the performance while consuming 5X less power.

According to this article, "Deep Vision announces its low-latency AI processor for the edge"

Deep Vision, a new AI startup that is building an AI inferencing chip for edge computing solutions, is coming out of stealth today. The six-year-old company’s new ARA-1 processors promise to strike the right balance between low latency, energy efficiency and compute power for use in anything from sensors to cameras and full-fledged edge servers.


AI Chip Startup Groq, Founded By Ex-Googlers, Raises $300 Million To Power Autonomous Vehicles And Data Centers

Jonathan Ross left Google to launch next-generation semiconductor startup Groq in 2016. Today, the Mountain View, California-based firm said that it had raised $300 million led by Tiger Global Management and billionaire investor Dan Sundheim’s D1 Capital as it officially launched into public view.

Kneron to Accelerate Edge AI Development with more than 10 Million USD Series A Financing

According to this article, "Gyrfalcon offers Automotive AI Chip Technology"

Gyrfalcon Technology Inc. (GTI), has been promoting matrix-based application specific chips for all forms of AI since offering their production versions of AI accelerator chips in September 2017. Through the licensing of its proprietary technology, the company is confident it can help automakers bring highly competitive AI chips to production for use in vehicles within 18 months, along with significant gains in AI performance, improvements in power dissipation and cost advantages.


Esperanto Unveils ML Chip with Nearly 1,100 RISC-V Cores

At the RISC-V Summit today, Art Swift, CEO of Esperanto Technologies, announced a new, RISC-V based chip aimed at machine learning and containing nearly 1,100 low-power cores based on the open-source RISC-V architecture.

According to this article, "Esperanto exits stealth mode, aims at AI with a 4,096-core 7nm RISC-V monster"

Although Esperanto will be licensing the cores they have been designing, they do plan on producing their own products. The first product they want to deliver is the highest TeraFLOP per Watt machine learning computing system. Ditzel noted that the overall design is scalable in both performance and power. The chips will be designed in 7nm and will feature a heterogeneous multi-core architecture.

SambaNova raises $676M at a $5.1B valuation to double down on cloud-based AI software for enterprises

SambaNova — a startup building AI hardware and integrated systems that run on it that only officially came out of three years in stealth last December — is announcing a huge round of funding today to take its business out into the world. The company has closed on $676 million in financing, a Series D that co-founder and CEO Rodrigo Liang has confirmed values the company at $5.1 billion.

Introducing SambaNova Systems DataScale: A New Era of Computing

SambaNova has been working closely with many organizations the past few months and has established a new state of the art in NLP. This advancement in NLP deep learning is illustrated by a GPU-crushing, world record performance result achieved on SambaNova Systems’ Dataflow-optimized system.

A New State of the Art in NLP: Beyond GPUs

SambaNova has been working closely with many organizations the past few months and has established a new state of the art in NLP. This advancement in NLP deep learning is illustrated by a GPU-crushing, world record performance result achieved on SambaNova Systems’ Dataflow-optimized system.

GreenWaves Technologies develops IoT Application Processors based on Open Source IP blocks enabling content understanding applications on embedded, battery-operated devices with unmatched energy efficiency. Our first product is GAP8. GAP8 provides an ultra-low power computing solution for edge devices carrying out inference from multiple, content rich sources such as images, sounds and motions. GAP8 can be used in a variety of different applications and industries.

Optical Chip Solves Hardest Math Problems Faster than GPUs

Optical computing startup Lightelligence has demonstrated a silicon photonics accelerator running the Ising problem more than 100 times faster than a typical GPU setup.

Lightmatter aims to reinvent AI-specific chips with photonic computing and $11M in funding

It takes an immense amount of processing power to create and operate the “AI” features we all use so often, from playlist generation to voice recognition. Lightmatter is a startup that is looking to change the way all that computation is done — and not in a small way. The company makes photonic chips that essentially perform calculations at the speed of light, leaving transistors in the dust. It just closed an $11 million Series A.

First Low-Power AI-Inference Accelerator Vision Processing Unit From Think Silicon To Debut at Embedded World 2018

TORONTO, Canada/NUREMBERG, Germany – FEB 21st, 2018 – Think Silicon®, a leader in developing ultra-low power graphics IP technology, will demonstrate a prototype of NEMA® xNN, the world’s first low-power ‘Inference Accelerator’ Vision Processing Unit for artificial intelligence, convolutional neural networks at Embedded World 2018.

Startup Puts AI Core in SSDs

Startup InnoGrit debuted a set of three controllers for solid-state drives (SSDs), including one for data centers that embeds a neural-network accelerator. They enter a crowded market with claims of power and performance advantages over rivals.

Innogrit Technologies Incorporated is a startup seting out to solve the data storage and data transport problem in artificial intelligence and other big data applications through innovative integrated circuit (IC) and system solutions: Extracts intelligence from correlated data and unlocks the value in artificial intelligence systems; Reduces redundancy in big data and improves system efficiency for artificial intelligence applications; Brings networking capability to storage devices and offers unparalleled performance at large scales; Performs data computation within storage devices and boosts performance of large data centers.

Kortiq is a startup providing "FPGA based Neural Network Engine IP Core and The scalable Solution for Low Cost Edge Machine Learning Inference for Embedded Vision". Recently, they revealed some comparison data. You can also find the Preliminary Datasheet of their AIScaleCDP2 IP Core on their website.

Hailo unveils Hailo-8, an edge chip custom-designed for AI workloads

......Hailo-8 is capable of 26 tera operations per second (TOPs) ...... In one preliminary test at an image resolution of 224 x 224, the Hailo-8 processed 672 frames per second compared with the Xavier AGX’s 656 frames and sucked down only 1.67 watts (equating to 2.8 TOPs per watt) versus the Nvidia chip’s 32 watts (0.14 TOPs per watt)......


Only last week, we did a thought experiment about how we should have streamlined chiplets for very specific purposes, woven together inside of a single package or across sockets and nodes, co-designed to specifically run very precise workflows because any general purpose processor – mixing elements of CPUs, GPUs, TPUs, NNPs, and FPGAs – would be suboptimal on all fronts except volume economics. We think that this extreme co-design for datacenter compute is the way the world will ultimately go, and we are just getting the chiplet architectures and interconnects together to make this happen. Radoslav Danilak, co-founder and chief executive officer of processor upstart Tachyum, is having absolutely none of that. And in fact, the Prodigy “universal processor” that Tachyum has designed is going in exactly in the opposite direction.

Tachyum Running Apache is a Key Milestone for Prodigy Universal Processor Software Stack

Semiconductor startup Tachyum Inc. today announced that it has completed another critical stage in software development by successfully achieving an Apache web server port to Prodigy Universal Processor Instruction Set Architecture (ISA). This latest milestone by Tachyum’s software team brings the company’s Prodigy Universal Processor one step closer to being customer-ready in anticipation of its commercial launch in 2021.

Startup AI Chip Passes Road Test

AlphaICs designed an instruction set architecture (ISA) optimized for deep-learning, reinforcement-learning, and other machine-learning tasks. The startup aims to produce a family of chips with 16 to 256 cores, roughly spanning 2 W to 200 W.

Syntiant: Analog Deep Learning Chips

Startup Syntiant Corp. is an Irvine, Calif. semiconductor company led by former top Broadcom engineers with experience in both innovative design and in producing chips designed to be produced in the billions, according to company CEO Kurt Busch.

Baidu Backs Neuromorphic IC Developer

MUNICH — Swiss startup aiCTX has closed a $1.5 million pre-A funding round from Baidu Ventures to develop commercial applications for its low-power neuromorphic computing and processor designs and enable what it calls “neuromorphic intelligence.” It is targeting low-power edge-computing embedded sensory processing systems.

Flex Logix has two paths to making a lot of money challenging Nvidia in AI

The programmable chip company scores $55 million in venture backing, bringing its total haul to $82 million

Preferred Networks develops a custom deep learning processor MN-Core for use in MN-3, a new large-scale cluster, in spring 2020

Dec. 12, 2018, Tokyo Japan – Preferred Networks, Inc. (“PFN”, Head Office: Tokyo, President & CEO: Toru Nishikawa) announces that it is developing MN-Core (TM), a processor dedicated to deep learning and will exhibit this independently developed hardware for deep learning, including the MN-Core chip, board, and server, at the SEMICON Japan 2018, held at Tokyo Big Site.

AI Startup Cornami reveals details of neural net chip

Stealth startup Cornami on Thursday revealed some details of its novel approach to chip design to run neural networks. CTO Paul Masters says the chip will finally realize the best aspects of a technology first seen in the 1970s.

AI chip startup offers new edge computing solution

Anaflash Inc. (San Jose, CA) is a startup company that has developed a test chip to demonstrate analog neurocomputing taking place inside logic-compatible embedded flash memory.

Optalysys launches world’s first commercial optical processing system, the FT:X 2000

Optalysys develops Optical Co-processing technology which enables new levels of processing capability delivered with a vastly reduced energy consumption compared with conventional computers. Its first coprocessor is based on an established diffractive optical approach that uses the photons of low-power laser light instead of conventional electricity and its electrons. This inherently parallel technology is highly scalable and is the new paradigm of computing.

Low-Power AI Startup Eta Compute Delivers First Commercial Chips

The firm pivoted away from riskier spiking neural networks using a new power management scheme

Eta Compute Debuts Spiking Neural Network Chip for Edge AI

Chip can learn on its own and inference at 100-microwatt scale, says company at Arm TechCon.

Achronix Rolls 7-nm FPGAs for AI

Achronix is back in the game of providing full-fledged FPGAs with a new high-end 7-nm family, joining the Gold Rush of silicon to accelerate deep learning. It aims to leverage novel design of its AI block, a new on-chip network, and use of GDDR6 memory to provide similar performance at a lower cost than larger rivals Intel and Xilinx.

Startup Runs AI in Novel SRAM

Areanna is the latest example of an explosion of new architectures spawned by the rise of deep learning. The debut of a whole new approach to computing has fired imaginations of engineers around the industry hoping to be the next Hewlett and Packard.

NeuroBlade Preps Inference Chip

Add NeuroBlade to the dozens of startups working on AI silicon. The Israeli company just closed a $23 million Series A, led by the founder of Check Point Software and with participation from Intel Capital.

Bill Gates just backed a chip startup that uses light to turbocharge AI

Luminous Computing has developed an optical microchip that runs AI models much faster than other semiconductors while using less power.

Chip startup Efinix hopes to bootstrap AI efforts in IoT

Six-year-old startup Efinix has created an intriguing twist on the FPGA technology dominated by Intel and Xiliinx; the company hopes its energy-efficient chips will bootstrap the market for embedded AI in the Internet of Things.

AIStorm raises $13.2 million for AI edge computing chips

David Schie, a former senior executive at Maxim, Micrel, and Semtech, thinks both markets are ripe for disruption. He — along with WSI, Toshiba, and Arm veterans Robert Barker, Andreas Sibrai, and Cesar Matias — in 2011 cofounded AIStorm, a San Jose-based artificial intelligence (AI) startup that develops chipsets that can directly process data from wearables, handsets, automotive devices, smart speakers, and other internet of things (IoT) devices. Raises $30 Million in Series A Investment Round Led by Dell Technologies Capital

SAN JOSE, Calif.--(BUSINESS WIRE), the company enabling high performance machine learning to go green, today announced its Machine Learning SoC (MLSoC) platform – the industry’s first unified solution to support traditional compute with high performance, lowest power, safe and secure machine learning inference. Delivering the highest frames per second per watt,’s MLSoC is the first machine learning platform to break the 1000 FPS/W barrier for ResNet-501. In customer engagements, the company has demonstrated 10-30x improvement in FPS/W through its automated software flow across a wide range of embedded edge applications, over today’s competing solutions. The platform will provide machine learning solutions that range from 50 TOPs@5W to 200 TOPs@20W, delivering an industry first of 10 TOPs/W for high performance inference.™ Introduces MLSoC™ – First Machine Learning Platform to Break 1000 FPS/W Barrier with 10-30x Improvement over Alternative Solutions, the company enabling high performance machine learning to go green, today announced its Machine Learning SoC (MLSoC) platform – the industry’s first unified solution to support traditional compute with high performance, lowest power, safe and secure machine learning inference. Delivering the highest frames per second per watt,’s MLSoC is the first machine learning platform to break the 1000 FPS/W barrier for ResNet-501. In customer engagements, the company has demonstrated 10-30x improvement in FPS/W through its automated software flow across a wide range of embedded edge applications, over today’s competing solutions. The platform will provide machine learning solutions that range from 50 TOPs@5W to 200 TOPs@20W, delivering an industry first of 10 TOPs/W for high performance inference.

Untether AI nabs $125M for AI acceleration chips

Untether AI, a startup developing custom-built chips for AI inferencing workloads, today announced it has raised $125 million from Tracker Capital Management and Intel Capital. The round, which was oversubscribed and included participation from Canada Pension Plan Investment Board and Radical Ventures, will be used to support customer expansion.

GrAI Matter Labs Reveals NeuronFlow Technology and Announces GrAIFlow SDK

GrAI Matter Labs (aka GML), a neuromorphic computing pioneer today revealed NeuronFlow – a new programmable processor technology – and announced an early access program to its GrAIFlow software development kit.

Rain Neuromorphics on Crunchbase

We build artificial intelligence processors, inspired by the brain. Our mission is to enable brain-scale intelligence.

Applied Brain Research on Crunchbase

ABR makes the world's most advanced neuromoprhic compiler, runtime and libraries for the emerging space of neuromorphic computing.

XMOS adapts Xcore into AIoT ‘crossover processor’

EE Times exclusive! The new chip targets AI-powered voice interfaces in IoT devices — “the most important AI workload at the endpoint.”

XMOS unveils, a powerful chip designed for AI processing at the edge

The latest is a crossover chip designed to deliver high-performance AI, digital signal processing, control, and input/output in a single device with prices from $1.

We design and produce AI processors and the software to run them in data centers. Our unique approach optimizes for inference with the focus on performance, power efficiency, and ease of use; and at the same time our approach enables cost-effective training.

We build high-performance AI inference coprocessors that can be seamlessly integrated into various computing platforms including data centers, servers, desktops, automobiles and robots.

Corerain provides ultra-high performance AI acceleration chips and the world's first streaming engine-based AI development platform.

Perceive emerges from stealth with Ergo edge AI chip

On-device computing solutions startup Perceive emerged from stealth today with its first product: the Ergo edge processor for AI inference. CEO Steve Teig claims the chip, which is designed for consumer devices like security cameras, connected appliances, and mobile phones, delivers “breakthrough” accuracy and performance in its class.

SimpleMachines, Inc. Debuts First-of-its-Kind High Performance Chip

As traditional chip makers struggle to embrace the challenges presented by the rapidly evolving AI software landscape, a San Jose startup has announced it has working silicon and a whole new future-proof chip paradigm to address these issues. The SimpleMachines, Inc. (SMI) team – which includes leading research scientists and industry heavyweights formerly of Qualcomm, Intel and Sun Microsystems – has created a first-of-its-kind easily programmable, high-performance chip that will accelerate a wide variety of AI and machine-learning applications.

NeuReality unveiled NR1-P, A novel AI-centric inference platform

NeuReality has unveiled NR1-P, a novel AI-centric inference platform. NeuReality has already started demonstrating its AI-centric platform to customers and partners. NeuReality has redefined today’s outdated AI system architecture by developing an AI-centric inference platform based on a new type of System-on-Chip (SoC).

NeuReality raises $8M for its novel AI inferencing platform

NeuReality, an Israeli AI hardware startup that is working on a novel approach to improving AI inferencing platforms by doing away with the current CPU-centric model, is coming out of stealth today and announcing an $8 million seed round.

Analog inference startup raises $10.6 million

The company is backed by Khosla Ventures and is developing its first generation of products for AI computing at the edge. The company raised $4.5 million shortly after its formation in March 2018, so the latest tranche brings the total raised to-date to $15.1 million

<a href=">Quadric Announces Unified Silicon and Software Platform Optimized for On-Device AI</a>

BURLINGAME, Calif., June 22, 2021 — Quadric (, an innovator in high-performance edge processing, has introduced a unified silicon and software platform that unlocks the power of on-device AI.

<a href=">EdgeQ reveals more details behind its next-gen 5G/AI chip</a>

5G is the current revolution in wireless technology, and every chip company old and new is trying to burrow their way into this ultra-competitive — but extremely lucrative — market. One of the most interesting new players in the space is EdgeQ, a startup with a strong technical pedigree via Qualcomm that we covered last year after it raised a nearly $40 million Series A.

<a href=">Innatera Unveils Neuromorphic AI Chip to Accelerate Spiking Networks</a>

Innatera, the Dutch startup making neuromorphic AI accelerators for spiking neural networks, has produced its first chips, gauged their performance, and revealed details of their architecture.

AI Chip Compilers

1. pytorch/glow
2. TVM:End to End Deep Learning Compiler Stack
3. Google Tensorflow XLA</strong>
4. Nvidia TensorRT
5. PlaidML
6. nGraph
7. MIT Tiramisu compiler
8. ONNC (Open Neural Network Compiler)
9. MLIR: Multi-Level Intermediate Representation
10. The Tensor Algebra Compiler (taco)
11. Tensor Comprehensions
12. PolyMage Labs
13. OctoML

AI Chip Benchmarks

1. DAWNBench:An End-to-End Deep Learning Benchmark and Competition Image Classification (ImageNet)
2. Fathom:Reference workloads for modern deep learning methods
3. MLPerf:A broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms. You can find MLPerf training results v1.0 here..
You can find MLPerf inference results v1.1 here..
You can find MLPerf HPC results v1.0 here..
You can find MLPerf training results v1.1 here..
4. AI Matrix
5. AI-Benchmark
6. AIIABenchmark
7. EEMBC MLMark Benchmark


1. FPGAs and AI processors: DNN and CNN for all
2. 12 AI Hardware Startups Building New AI Chips
3. Tutorial on Hardware Architectures for Deep Neural Networks
4. Neural Network Accelerator Comparison
5. "White Paper on AI Chip Technologies 2018". You can download it from here, or Google drive.
5. "What We Talk About When We Talk About AI Chip". #1, #2, #3, #4
6. AI Chip Paper List
7. TPU vs GPU vs Cerebras vs Graphcore: A Fair Comparison between ML Hardware