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 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

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 / https://www.phoronix.com/scan.php?page=news_item&px=hipSYCL-New-Lite-Runtime https://software.intel.com/content/www/us/en/develop/articles/interoperability-dpcpp-sycl-opencl.html https://www.renesas

© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon/Intel Confidentia Presenter: Zhi Chen, Cody Yu Amazon SageMaker Neo, Deep Engine Science Bring Your Own Codegen to TVM Chip© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example showcase: Intel MKL-DNN (DNNL) library 1. Import packages import numpy as np from tvm import relay 2. Load a pretrained 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

Source Code DPC++ Uses LLVM/Clang Part of oneAPI hipSYCL Multiple Backends Any CPU Intel CPUs Intel GPUs Intel FPGAs AMD GPUs Any CPU SYCL enables Khronos to influence ISO C++ to (eventually) support integration and deployment of multiple acceleration technologies Level Zero Intel GPUs NVIDIA GPUs Level Zero Intel GPUs AMD GPUs New, not experimental anymore, and works on Ponte Vecchio deployment of multiple acceleration technologies VEO Intel 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++

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 ISA 표준으로 만들려고 시도했다. 1.2 모듈형 vs. 증분형 ISA Intel was betting its future on a high-end microprocessor, but that was still years away. To counter Zilog, Intel developed a stop-gap processor and called it 4.5 그림 1.6: ISA 매뉴얼의 페이지와 단어 수. [Waterman and Asanovi´c 2017a], [Waterman and Asanovi´c 2017b], [Intel Corporation 2016], [ARM Ltd. 2014]. 1주일에 40시간에 대하여 분당 200단어를 읽는다고 가정할 때 시간 및 주. [Baumann 2017]의 그림1의

LBA sector 0 (512 bytes). Recent PCs with Unified Extensible Firmware Interface (UEFI), including Intel-based Macs, use GUID Partition Table (GPT) scheme to hold disk partitioning data not in the first software now and are included in the normal Debian kernel packages in the main area. • GPU driver – Intel GPU driver (main) – AMD/ATI GPU driver (main) – NVIDIA GPU driver (main for nouveau driver, and on the device attach to the target system (e.g., CPU microcode, rendering code running on GPU, or FPGA / CPLD data, …). Some firmware packages are available as free software but many firmware packages

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

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