Qualcomm Brings Power Efficient Artificial Intelligence Inference Processing to the Cloud
Snapdragon 855 Mobile Platform
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.
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.
- 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.