Adapteva bitcoin mining

Deep-Learning-Processor-List A list of ICs and IPs for AI, Machine Learning and Deep Learning. Add the latest information of Adapteva bitcoin mining’s “Project Trillium” and Imagination’s NNA. Add news of Google TPU3 and Microsoft Brainwave.

Add benchmark, MLPerf in Reference section. Add information from PEZY: First Use of TCI. Add two benchmarks, DAWNBench and Fathom in Reference section. Add two optical AI computing company Lightelligence and Lightmatter . Add news from Alibaba and Facebook .

Add Videantis in IP vendor section. Add Google announced the open beta of its TPU2. Add Amazon’s custom AI chip for future Echo devices. Intel is also planning in integrating into the Phi platform via a Knights Crest project. As our Intel CEO Brian Krzanich discussed earlier today at Wall Street Journal’s D. 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. 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.

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. 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. We did not see Early Access verion yet. Hopefully, the general release will be avaliable on Sep. 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. 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.

The new Exynos 9810 brings premium features with a 2. The soon to be released AMD Radeon Instinct MI25 is promising 12. 3 TFlops of SP or 24. If your calculations are amenable to Nvidia’s Tensors, then AMD can’t compete. Tesla is reportedly developing its own processor for artificial intelligence, intended for use with its self-driving systems, in partnership with AMD. 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. In terms of basic building blocks, its transistor count is 5. POWER9 will be the first commercial platform loaded with on-chip support for NVIDIA’s next-generation NVLink, OpenCAPI 3. These technologies provide a giant hose to transfer data. 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 is suited for ADAS, NCAP front camera, object detection and recognition, surround view, machine learning and sensor fusion applications. 5 billion consumer products a year across smartphones, smart homes, autos and more. In this article,we can find more details about NPU in Kirin970. RK3399Pro adopted exclusive AI hardware design. Its NPU computing performance reaches 2. The original 700MHz TPU is described as having 95 TFlops for 8-bit calculations or 23 TFlops for 16-bit whilst drawing only 40W.