- Add startup DinoPlusAI.
- Add startup XMOS.
- Add news of Eta Compute.
- Add news of BrainChip and Tachyum.
- Add news of Enflame's DTU data center training chip.
- Updates of AImotive.
- Add news of Cerebras and Graphcore and Intel.
- Thinci is now Blaize
- Check out MLPerf Inference v0.5 Results.
- Add startup Rain Neuromorphics and Applied Brain Research.
- Add startup Untether AI and GrAI Matter Lab.
- Add startup SiMa.ai.
- Remove DeepScale who was acquired by Tesla.
- Add news of Alibaba's cloud inference chip Hanguang 800.
- Add startup Luminous Computing, Efinix, AISTORM.
- Add news of NVDLA Deep Learning Inference Compiler.
- Add news of Intel's AI chip.
- Add news of Cerebras's huge WSE AI chip.
- Add startup Neuroblade.
- Add EEMBC MLMark Benchmark in AI Chip Benchmarks.
- Add news of InnoGrit.
- Add startup Areanna AI.
- Add links to TSING MICRO, Ambarella and Black Sesame.
- Add Chinese startup WITINMEM.
- MLPerf v0.5 Inference Benchmarks are here.
- Add news of Renesas Electronics's PIM AI Chip.
- Add news of Habana's Gaudi AI Training Chip.
- Add news of LG's AI Chip.
- Add The Tensor Algebra Compiler (taco).
- Add news of Achronix’s Speedster7t FPGA.
- Add news of Hailo’s Hailo-8 chip.
- Add MLIR in AI Compiler section and news of Tesla's FSD chip..
- Add startup Eta Compute.
- Add Compiler ONNC.
- Add Chinese startup EEasy Tech.
- Add startup Optaylsys.
- Add a new section of AI Chip Compilers.
- Add Chinese startup Enflame.
- Add news of Google Edge TPU.
- Add a new section of AI Chip Benchmarks.
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Intel FPGA OpenCL and Solutions. Intel's Loihi test chip is the First-of-Its-Kind Self-Learning Chip.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.
The Loihi research test chip includes digital circuits that mimic the brain’s basic mechanics, making machine learning faster and more efficient while requiring lower compute power. Neuromorphic chip models draw inspiration from how neurons communicate and learn, using spikes and plastic synapses that can be modulated based on timing. This could help computers self-organize and make decisions based on patterns and associations.
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)
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.
NVIDIA TESLA T4 TENSOR CORE GPU
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.
Nvidia’s DGX-2 System Packs An AI Performance Punch
Building Bigger, Faster GPU Clusters Using NVSwitches
Nvidia launched its second-generation DGX system in March. In order to build the 2 petaflops half-precision DGX-2, Nvidia had to first design and build a new NVLink 2.0 switch chip, named NVSwitch. While Nvidia is only shipping NVSwitch as an integral component of its DGX-2 systems today, Nvidia has not precluded selling NVSwitch chips to data center equipment manufacturers.
Nvidia's latest GPU can do 15 TFlops of SP or 120 TFlops with its new Tensor core architecture which is a FP16 multiply and FP32 accumulate or add to suit ML.
Nvidia is packing up 8 boards into their DGX-1for 960 Tensor TFlops.
Nvidia Volta - 架构看点 gives some insights of Volta architecture.
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.
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”.
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."
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.
S32 AUTOMOTIVE PLATFORM
S32 AUTOMOTIVE PLATFORM
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.
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 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
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.
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
Google的神经网络处理器专利
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 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.
AN EARLY LOOK AT BAIDU’S CUSTOM AI AND ANALYTICS PROCESSOR
We’ve written much over the last few years about the company’s emphasis on streamlining deep learning processing, most notably with GPUs, but Baidu has a new processor up its sleeve called the XPU. For now, the device has just been demonstrated in FPGA, but if it continues to prove useful for AI, analytics, cloud, and autonomous driving the search giant could push it into a full-bore ASIC.
Baidu creates Kunlun silicon for AI
A pair of chips from the Chinese search giant are aimed at cloud and edge use cases. The company said it started developing a field-programmable gate array AI accelerator in 2011, and that Kunlun is almost 30 times faster. The chips are made with Samsung's 14nm process, have 512GBps memory bandwidth, and are capable of 260 tera operations per second at 100 watts.
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.
HPE DEVELOPING ITS OWN LOW POWER “NEURAL NETWORK” CHIPS
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’s new self-driving chip is here, and this is your best look yet
...And today, at Tesla’s Autonomy Investor Day in Palo Alto, California, the company gave the world its first, detailed glimpse at what Musk is now calling “the best chip in the world” — a 260 square millimeter piece of silicon, with 6 billion transistors, that the company claims offers 21 times the performance of the Nvidia chips it was using before.
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
LG TO ACCELERATE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE WITH OWN AI CHIP
New AI Processor with LG Neural Engine Designed for Use in Various Products Including Robot Vacuum Cleaners, Washing Machines and Refrigerators
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 Announces First PowerVR Series2NX Neural Network Accelerator Cores: AX2185 and AX2145
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
神经网络DSP核的一桌麻将终于凑齐了
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.
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.
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.
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.
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.
TO POWER AI, THIS STARTUP BUILT A REALLY, REALLY BIG CHIP
New artificial intelligence company Cerebras Systems is unveiling the largest semiconductor chip ever built. The Cerebras Wafer Scale Engine has 1.2 trillion transistors, the basic on-off electronic switches that are the building blocks of silicon chips. Intel’s first 4004 processor in 1971 had 2,300 transistors, and a recent Advanced Micro Devices processor has 32 billion transistors.
Cerebras Systems unveils a record 1.2 trillion transistor chip for AI
Computer chips are usually small. The processor that powers the latest iPhones and iPads is smaller than a fingernail, and even the beefy devices used in cloud servers aren’t much bigger than a postage stamp. Then there’s a new chip from startup Cerebras: It’s bigger than an iPad all by itself. The silicon monster is almost 22 centimeters—roughly 9 inches—on each side, making it likely the largest computer chip ever, and a monument to the tech industry’s hopes for artificial intelligence. Cerebras plans to offer it to tech companies trying to build smarter AI more quickly.
Wave’s Compute Appliance is capable to run TensorFlow at 2.9 PetaOPS/sec on their 3RU appliance. Wave refers to their processors at DPUs and an appliance has 16 DPUs. Wave uses processing elements it calls Coarse Grained Reconfigurable Arrays (CGRAs). It is unclear what bit width the 2.9 PetaOPS/s is referring to. Some details can be fund in their white paper.
After HotChips 2017, in the next plateform article "First In-Depth View of Wave Computing’s DPU Architecture, Systems", more details were discussed.
Microsoft and Graphcore Colleborate to Accelerate Artificial Intelligence
Today we are very excited to share details of our collaboration with Microsoft, announcing preview of Graphcore® Intelligence Processing Units (IPUs) on Microsoft Azure. This is the first time a major public cloud vendor is offering Graphcore IPUs which are built from the ground up to support next generation machine learning. It’s a landmark moment for Graphcore and is testament to the maturity of our patented IPU technology, both of our IPU hardware and of our Poplar® software stack.
解密又一个xPU：Graphcore的IPU give some analysis on its IPU architecture.
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 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.
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 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.
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.
Deep Vision is bulding low-power chips for deep learning. Perhaps one of these papers by the founders have clues, "Convolution Engine: Balancing Efficiency & Flexibility in Specialized Computing" [2013] and "Convolution Engine: Balancing Efficiency and Flexibility in Specialized Computing" [2015].
Groq is founded by Ex-googlers, who designed Google TPU.
Groq's website claims that its first chip will run 400 trillion operations per second with 8TOP/s per Watt power efficiency.
Face Recognition System “K-Eye” Presented by KAIST
从ISSCC Deep Learning处理器论文到人脸识别产品
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.
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.
According to the linkedin page of its CEO, former SPARC developer in ORACLE, SambaNova Systems is a computing startup focused on building machine learning and big data analytics platforms. SambaNova's software-defined analytics platform enables optimum performance for any ML training, inference or analytics models.
The red-hot AI hardware space gets even hotter with $56M for a startup called SambaNova Systems
SambaNova is the product of technology from Kunle Olukotun and Chris Ré, two professors at Stanford, and led by former Oracle SVP of development Rodrigo Liang, who was also a VP at Sun for almost 8 years.
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.
Light-Powered Computers Brighten AI’s Future
Optical computers may have finally found a use—improving artificial intelligence
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.
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 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)......
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.
HABANA LABS Announces Gaudi AI Training Processor
TEL-AVIV, ISRAEL and SAN JOSE, CA–June 17, 2019 – Habana Labs, Ltd. (www.habana.ai), a leading developer of AI processors, today announced the Habana Gaudi™ AI Training Processor. Training systems based on Gaudi processors will deliver an increase in throughput of up to four times over systems built with equivalent number GPUs.
You can also find the reports in the media
The Goya chip can process 15,000 ResNet-50 images/second with 1.3-ms latency at a batch size of 10 while running at 100 W. That compares to 2,657 images/second for an Nvidia V100 and 1,225 for a dual-socket Xeon 8180. At a batch size of one, Goya handles 8,500 ResNet-50 images/second with a 0.27-ms latency.
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.
AI startup Flex Logix touts vastly higher performance than Nvidia
Four-year-old startup Flex Logix has taken the wraps off its novel chip design for machine learning. CEO Geoff Tate describes how the chip may take advantage of an "explosion" of inferencing activity in "edge computing," and how Nvidia can't compete on performance.
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.
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.
SiMa.ai, 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, SiMa.ai’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 raises $20 million to develop machine learning inferencing hardware
Untether AI, a Toronto-based startup that’s developing high-efficiency, high-performance chips for AI inferencing workloads, this morning announced that it has raised a $20 million series A round, following a small seed investment. Radical Ventures joined Intel Capital and other investors in the round, with Radical Ventures partner Tomi Poutanen joining as a board member.
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 Xcore.ai, a powerful chip designed for AI processing at the edge
The latest xcore.ai 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.
1. pytorch/glow
2. TVM:End to End Deep Learning Compiler Stack
3. Google Tensorflow XLA
4. Nvidia TensorRT
5. PlaidML
6. nGraph
7. MIT Tiramisu compiler
8. ONNC (Open Neural Network Compiler)
9. Multi-Level Intermediate Representation
10. The Tensor Algebra Compiler (taco)
- DAWNBench:An End-to-End Deep Learning Benchmark and Competition Image Classification (ImageNet)
- Fathom:Reference workloads for modern deep learning methods
- MLPerf:A broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms. You can find MLPerf v0.5 results here.. MLPerf Inference Benchmarks is here.
- AI Matrix
- AI-Benchmark
- AIIABenchmark
- EEMBC MLMark Benchmark
- FPGAs and AI processors: DNN and CNN for all
- 12 AI Hardware Startups Building New AI Chips
- Tutorial on Hardware Architectures for Deep Neural Networks
- Neural Network Accelerator Inference
- "White Paper on AI Chip Technologies 2018". You can download it from here, or Google drive.