A List of Chip/IP for Deep Learning
Machine Learning, especially Deep Learning technology is driving the evolution of artificial intelligence (AI). At the beginning, deep learning has primarily been a software play. Start from the year 2016, the need for more efficient hardware acceleration of AI/ML/DL was recognized in academia and industry. This year, we saw more and more players, including world’s top semiconductor companies as well as a number of startups, even tech giants Google, have jumped into the race.
I believe that it could be very interesting to look at them together. So, I build this list of AI/ML/DL ICs and IPs on Github and keep updating. If you have any suggestion or new information, please let me know.
The companies and products in the list are organized into five categories as shown in the following table.
- Add news from Alibaba and Facebook .
- Add Videantis in IP vendor section.
- Add Graphcore’s latest talk.
- Add Nvidia DGX-2 System and NvSwitch.
- Add SambaNova Systems.
- Add Nokia ReefShark.
- Add GreenWaves GAP8.
- Add Qualcomm AI Engine.
- Add Benchmarking Google’s new TPUv2.
I. IC Vendors
Intel purchased Nervana Systems who was developing both a GPU/software approach in addition to their Nervana Engine ASIC. Comparable performance is unclear. Intel is also planning in integrating into the Phi platform via a Knights Crest project. NextPlatformsuggested the 2017 target on 28nm may be 55 TOPS/s for some width of OP. There is a NervanaCon Intel has scheduled for December, so perhaps we’ll see the first fruits then.
As our Intel CEO Brian Krzanich discussed earlier today at Wall Street Journal’s D.Live event, Intel will soon be shipping the world’s first family of processors designed from the ground up for artificial intelligence (AI): the Intel® Nervana™ Neural Network Processor family (formerly known as “Lake Crest”). This family of processors is over 3 years in the making, and on behalf of the team building it, I’d like to share a bit more insight on the motivation and design behind the world’s first neural network processor.
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.
MYRIAD 2 IS A MULTICORE, ALWAYS-ON SYSTEM ON CHIP THAT SUPPORTS COMPUTATIONAL IMAGING AND VISUAL AWARENESS FOR MOBILE, WEARABLE, AND EMBEDDED APPLICATIONS. THE VISION PROCESSING UNIT INCORPORATES PARALLELISM, INSTRUCTION SET ARCHITECTURE, AND MICROARCHITECTURAL FEATURES TO PROVIDE HIGHLY SUSTAINABLE PERFORMANCE EFFICIENCY ACROSS A RANGE OF COMPUTATIONAL IMAGING AND COMPUTER VISION APPLICATIONS, INCLUDING THOSE WITH LOW LATENCY REQUIREMENTS ON THE ORDER OF MILLISECONDS.
Myriad™ X is the first VPU to feature the Neural Compute Engine — a dedicated hardware accelerator for running on-device deep neural network applications. Interfacing directly with other key components via the intelligent memory fabric, the Neural Compute Engine is able to deliver industry leading performance per Watt without encountering common data flow bottlenecks encountered by other architectures.
Intel’s Loihi test chip is the First-of-Its-Kind Self-Learning Chip.
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.
In a blog, “We are making on-device AI ubiquitous” shows its AI road map.
Qualcomm Artificial Intelligence (AI) Engine, which is comprised of several hardware and software components to accelerate on-device AI-enabled user experiences on select Qualcomm® Snapdragon™ mobile platforms. The AI Engine will be supported on Snapdragon 845, 835, 821, 820 and 660 mobile platforms, with cutting-edge on-device AI processing found in the Snapdragon 845.
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.
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.
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.
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 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.
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 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.”
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.
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.
Through a combination of hardware and software, an AI processing unit (APU ; Artificial intelligence Processing Unit ), and NeuroPilot SDK, MediaTek will bring AI across its wide-ranging technology portfolio — a portfolio that powers 1.5 billion consumer products a year across smartphones, smart homes, autos and more.
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
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.
II. Tech Giants & HPC Vendors
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.
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.
Other references are:
Google TPU 揭密
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.
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.
Wired did a nice story on the MSFT use of FPGAs too, “Microsoft Bets Its Future on a Reprogrammable Computer Chip”.
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.
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.
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.
Alibaba is developing its own neural network chip, the Ali-NPU, which will be used in AI applications, such as image video analysis, machine learning, and other scenarios.
FPGA Cloud server (Beta) is an computing instance of a field-programmable gate array (FPGA) that allows users to easily create FPGA design in minutes and create custom, dedicated hardware accelerators based on the Alibaba Cloud Elastic Computing Framework.
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.
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.
FPGA Cloud Compute is open for beta test
FPGA Accelerated Cloud Server, high performance FPGA instance is open for beta test.
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.
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.
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.
Imagination reveals PowerVR Neural Network Accelerator (NNA) with 2x the performance and half the bandwidth of nearest competitor
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
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.
Dec. 20, Horizon Robotics annouced two chip products, “Journey” for ADAS and “Sunrise” for Smart Cameras.
October 19, 2017, San Francisco, USA — Horizon Robotics, a leading global Artificial Intelligence (AI) startup, today announced during Intel Capital’s CEO Showcase that it has received investment from Intel Capital. Harvest Investments will join the round as a co-investor with participation from existing shareholders including Morningside Venture Capital, Hillhouse Capital, Wu Capital and Linear Ventures. The Company expects that its A-plus series funding round will total approximately US$100 million upon closing.
DeePhi Tech has the cutting-edge technologies in deep compression, compiling toolchain, deep learning processing unit (DPU) design, FPGA development, and system-level optimization. DeePhi has the Deep Neural Network Development Kit, DNNDK, which is a deep learning software development kit aimed at simplifying and accelerating deep learning applications.
This nextplatform arcicle “FPGA Startup Gathers Funding Force for Merged Hyperscale Inference” gave more information of the company.
Domestic artificial intelligent startup DeePhi Tech announced on Tuesday at a products launch that it has completed a Series A+ of financing for about $40 million. This round of financing was led by Alibaba’s financial affiliate Ant Financial Services Group and Samsung, with China Merchants Venture and China Growth Capital participating as fellow investors.
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.
V. Startups Worldwide
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.
Graphcore raised $30M of Series-A late last year to support the development of their Intelligence Processing Unit, or IPU. Resently, co-founder and Chief Technology Officer, Simon Knowles, was invited to give a talk at the 3rd Research and Applied AI Summit (RAAIS) in London, showing interesting ideas behind their processor.
In a resent post, Graphcore shows “Preliminary IPU Benchmarks”
Simon Knowles, Graphcore CTO, spoke at the Scaled Machine Learning Conference at Stanford in March about ‘Scaling Throughput Processors for Machine Intelligence’.
解密又一个xPU：Graphcore的IPU give some analysis on its IPU architecture.
Pezy-SC and Pezy-SC2 are the 1024 core and 2048 core processors that Pezy develop. The Pezy-SC 1024 core chip powered the top 3 systems on the Green500 list of supercomputers back in 2015. The Pezy-SC2 is the follow up chip that is meant to be delivered by now, but details are scarce yet intriguing,
“PEZY-SC2 HPC Brick: 32 of PEZY-SC2 module card with 64GB DDR4 DIMM (2.1 PetaFLOPS (DP) in single tank with 6.4Tb/s” It will be interesting to see what 2,048 MIMD MIPS Warrior 64-bit cores can do. In the June 2017 Green500 list, a Nvidia P100 system took the number one spot and there is a Pezy-SC2 system at number 7. So the chip seems alive but details are thin on the ground. Motoaki Saito is certainly worth watching.
Their product page has since June 2016 gone missing in action. Not sure what they are up to with the $100M they put into their MIMD architecture. It was described at the time as having 256 tiny DSP, or tDSP, cores on each ASIC along with an ARM controller suitable for sparse matrix processing in a 35W envelope.
Since KnuEdge “emerged from stealth” last year, the company has gone quiet and not offered up any additional information about what they’ve been up to. According to an article in VentureBeat, we know that KnuEdge has already been generating revenue and that they were considering raising more funding this year in addition to the $100 million in “patient money” they have already raised. Their website contains next to no information aside from employee profiles. At an Xconomy conference a few weeks ago, the Company talked about “cloud-based machine intelligence as a service” that is “supposed to be rolled out sometime this year“.
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.
ThinCI is developing vision processors from Sacremento with employees in India too. They claim to be at the point of first silicon, Thinci-tc500, along with benchmarking and winning of customers already happening. Apart from “doing everything in parallel” we have little to go on.
Founded in 2010, Eldorado Hills, California startup ThinCI has taken in an undisclosed amount of funding to develop a technology that will bring vision processing to all devices. The ability for smart devices to have functionality like computer vision that doesn’t require regular communication to the cloud is referred to as “edge computing” or “fog computing”. That’s where ThinCI wants to play.
Koniku’s web site shows “We are building co-processors made of biological neurons.”
Adapteva: “Adapteva tapes out Epiphany-V: A 1024-core 64-bit RISC processor.” Andreas Olofsson taped out his 1024 core chip late last year and we await news of its performance. Epiphany-V has new instructions for deep learning and we’ll have to see if this memory-controller-less design with 64MB of on-chip memory will have appropriate scalability. The impressive efficiency of Andrea’s design and build may make this a chip we can all actually afford, so let’s hope it performs well.
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.
Despite many promises,Kalray has not progressed their chip offering beyond the 256 core beast covered back in 2015, “Kalray — new product meander.” Kalray is advertising their product as suitable for embedded self-driving car applications. Kalray has a Kalray Neural Network(KaNN) software package and claims better efficiency than GPUs with up to 1 TFlop/s on chip.
Kalrays NN fortunes may improve with an imminent product refresh and just this month Kalray completed a new funding that raised $26M. The new Coolidge processor is due in mid-2018 with 80 or 160 cores along with 80 or 160 co-processors optimised for vision and deep learning.
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.
Speaking of chips, AImotive and partner VeriSilicon are in the process of designing a 22 nm FD-SOI test chip, which is forecast to come out of GlobalFoundries’ fab in Q1 2018 (Figure 4). It will feature a 1 TMAC/sec aiWare core, consuming approximately 25 mm2 of silicon area; a Vivante VIP8000-derivative processor core will inhabit the other half of the die, and between 2–4 GBytes of DDR4 SDRAM will also be included in the multi-die package. The convolution-tailored LAM in this test chip, according to Feher, will have the following specifications (based on preliminary synthesis results): 2,048 8x8 MACs Logic area (including input/output buffering logic, LAM control and MACs): 3.45mm2 Memory (on-chip buffer): in the range of 5–25mm2 depending on configuration (10–50 Mbits). Another interesting activity of Aimotive is Neural Network Exchange Format (NNEF).
Leapmind is carrying out research on original chip architectures in order to implement Neural Networks on a circuit enabling low power DeepLearning
While it is not actually possible to pick a worse name for your startup than “krtkl”, at least the product name is manageable. Snickerdoodle is “reconfigurable hardware for building intelligent systems” (think Raspberry Pi). 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). this video is the description and demos of NovuMind FPGA AI Accelerator.
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”  and “Convolution Engine: Balancing Efficiency and Flexibility in Specialized Computing” .
Here is the demo of their chip, usb dongle and development board.
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.
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.