INDUSTRIES, INCORPORATED ACCELERATED VISUAL PERCEPTION LIANGFU CHEN 11/16/2019 DEPLOY VTA ON INTEL FPGA©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 2 Moore’s Law is Slowing Down MOTIVATION©2019 DE10-Nano DEPLOY VTA ON INTEL FPGA©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 5 Software - CMA Contiguous Memory Allocation – Linux Kernel DEPLOY VTA ON INTEL FPGA https://pynq.readthedocs.io/en/v2 INCORPORATED 6 Software - CMA Contiguous Memory Allocation – Linux Kernel Module DEPLOY VTA ON INTEL FPGA Setup Environment Variables Navigate to 3rdparty/cma and build kernel module Copy kernel module

Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Target Device General Devices (CPU/GPU/FPGA) Mark supported operators or subgraphs 1. Implement an operator-level annotator, OR 2. Implement Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Target Device General Devices (CPU/GPU/FPGA) Mark supported operators or subgraphs 1. Implement extern operator functions, OR 2. Implement a Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Target Device General Devices (CPU/GPU/FPGA) Mark supported operators or subgraphs 1. Implement extern operator functions, OR 2. Implement a

Creative Commons Attribution 4.0 International License SYCL Single Source C++ Parallel Programming GPU FPGA DSP Custom Hardware GPU CPU CPU CPU Standard C++ Application Code C++ Libraries ML Frameworks Fusion can give better performance on complex apps and libs than hand-coding AI/Tensor HW GPU FPGA DSP Custom Hardware GPU CPU CPU CPU AI/Tensor HW Other BackendsSYCL 2020 is here! Open Standard -generation-supercomputers/ https://research-portal.uws.ac.uk/en/publications/trisycl-for-xilinx-fpga https://www.imaginationtech.com/news/press-release/tensorflow-gets-native-support-for-powervr-gp

core(0-13) Offline jobs: shared core(0-15) cpu.share 2 exclusive ������ ������ ����� Co-location GPU FPGA relatime - More resource dimension - Expand Alibaba internal co-location scale (Fuxi & sigma) �����������

Real Time Hard Real Time Simple System Don’t care None Complicated System Operating system FPGA/Chip + CPU with operating systemLet’s review a system and decide if an operating system is

CPUs NEC VEs neoSYCL SX-AURORA TSUBASA TBB Any CPU Samsung PIMS XILINX Versal ACAP LLVM IR FPGA LLVM IR HLS Experimental DPC++ fork DPC++ fork MLIR Inteon Poligeist SYCL MLIR Bisheng

don’t care, nor do we need to! ● if it uses a GPU, we don’t care, nor do we need to! ● if it uses an FPGA or a SoC, we don’t care, nor do we need to!function abstraction std::string SpellCheck(std::string

target device of DNN processing. Now support `cpu`(default), `fp32`, `fp16`, `vpu`, `vulkan` and `fpga`. More detailed contents please refer to https://docs.opencv.org/4.1.1/d6/d0f/group__dnn.html#ga709af7692ba29788

제어기에서부터 가장 빠른 고성능 컴퓨터에 이르기까지 모든 종류의 프로세서에 적합해야 한다. • 다양하고 유명한 소프트웨어 스택 및 프로그래밍 언어와 함께 잘 동작해야 한다. • FPGA(Field-Programmable Gate Arrays), ASIC(Application-Specific Integrated Cir- cuits), 풀 커스텀 칩, 심지어는 변경할 수 없을 때인 명령어가 최종 확정된 이후가 아니라 이전에 발생한다는 것을 의미할 것이다. 이상적인 상황에 소수의 회원들이 비준되기 전에 그 제안을 구현할 것이고, 이는 FPGA에서 훨씬 더 쉽게 구현된다. RISC-V 재단 위원회를 통한 명령어 확장 제안도 상당한 양의 작업이 될 것이고 이는 x86-32에서 발생했던 것(1장에 있는 4페이지의 그림 fnmsub.s . . . . . . Floating-point fused negative multiply-subtract single- precision를 함께 참고, 169 FPGA . . . Field-Programmable Gate Array를 함께 참고, 2 frcsr. . . . . . . . . . . . . . . . . . . . . .37

target device of DNN processing. Now support `cpu`(default), ˓→`fp32`, `fp16`, `vpu`, `vulkan` and `fpga`. More detailed contents please refer to ˓→https://docs.opencv.org/4.1.1/d6/d0f/group__dnn.html