## FPGA CNN Accelerator and TVM Elliott Delaye EXILINX ## TVM Target devices and models HW Platforms   © Copyright 2018 Xilinx ## Registering external accelerator function @reg.register_compute("accel", level=15) def compute_accel(attrs, inputs, outputs):

||0|1|0|| ||||||  ## CNN on feature maps 

4_2.jpg) ### 1. 背景知识 2017年google的机器翻译团队在NIPS上发表了Attention is all you need的文章，开创性地提出了在序列转录领域，完全抛弃CNN和RNN，只依赖Attention-注意力结构的简单的网络架构，名为Transformer；论文实现的任务是机器翻译。 ## Transformer结构  ### 1. 背景知识 ## 为什么需要用transformer CNN（如ResNet）是图像分类的最佳解决方案。 如果预训练的数据集足够大（至少一亿张图像），则Vision Transformer（ViT）将击败CNN（小幅度） Vision Transformer（ViT）实际上就是Transformer的encode网络。 Image 模型介绍 03 模型训练策略 04 模型的缺点与改进 05 模型的代码实现 ### 4. 模型缺点与改进 ## V iT缺点 Vision Transformer比CNN具有更少的图像特异性归纳偏差。 在CNN中，局部性、二维邻域结构和平移等方差被融入到整个模型的每一层中。 在ViT中，只有MLP层是局部的、平移等变的，而自注意层是全局的。 二维邻域结构的使用非常少：在模型的开始

shortcut keys: accelerator keys and hotkeys. Both are used to speed up working in KiCad by using the keyboard instead of the mouse to change commands. ##### 3.1.1. Accelerator keys Accelerator keys have the happen until the left mouse button is clicked. Use an accelerator key when you want to enter a command mode but do not want any immediate action. Accelerator keys are shown on the right side of all menu panes: |Symbol||||Shift+A| |Power Port||||Shift+P| |Wire||||Shift+W| ##### 3.1.2. Hotkeys A hotkey is equal to an accelerator key plus a left mouse click. Using a hotkey starts the command immediately at the current cursor

引脚的强有力的情况。一旦在 PCB 中做出选择，则该更改将被推回原理图。 ## 第 3 章 使用 KiCad #### 3.1. 快捷键 KiCad 有两种相关但不同的快捷键: 快捷键(accelerator keys) 和热键(hotkeys)。两者都用于通过使用键盘而不是鼠标来执行命令以提高使用 KiCad 的生产效率。 ##### 3.1.1. 快捷键 快捷键的效果与单击菜单或工具栏图标的效果相同: #encoding utf-8 # COMP DEF COMP U 0 40 Y Y 1 F N F0 "U" -1800 -100 50 H V C CNN F1 "COMP" -1800 100 50 H V C CNN DRAW S -2250 -800 -1350 800 0 0 0 N S -450 -800 450 800 0 0 0 N X PIN1 1 -2550

力的情况。一旦在 PCB 中做出选择，则该更改将被推回原理图。 # Chapter 3 # 使用 KiCad ### 3.1 快捷键 KiCad 有两种相关但不同的快捷键：快捷键 (accelerator keys) 和热键 (hotkeys)。两者都用于通过使用键盘而不是鼠标来执行命令以提高使用 KiCad 的生产效率。 #### 3.1.1 快捷键 快捷键的效果与单击菜单或工具栏图标 #encoding utf-8 # COMP DEF COMP U 0 40 Y Y 1 F N FO "U" -1800 -100 50 H V C CNN F1 "COMP" -1800 100 50 H V C CNN DRAW S -2250 -800 -1350 800 0 0 0 N S -450 -800 450 800 0 0 0 N X PIN1 1 -2550

flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 1, 5, 1.2, 0) # Hardware Accelerator Code import numpy as np frame1 = cv2.imread('./images/vtest/scene00002.png') prvs = cv2 /e/3/3/4/e334f61741573221602c5a3234497644/p29_12.jpg) xfDNN Middleware, Tools and Runtime xDNN CNN Processing Engine Deploy ➢ 4x higher real time inference over GPUs ![Image](/uploads/document

supported in Neuron SDK are FP16 and BF16. ## 1.5.1 Requirements - OS: Linux - Python: 3.8-3.11 - Accelerator: NeuronCore_v2 (in trn1/inf2 instances) - Pytorch 2.0.1/2.1.1 - AWS Neuron SDK 2.16/2.17 (Verified "float" # Create a BART encoder/decoder model instance llm = LLM( model="facebook/bart-large-cnn", dtype=dtype, ) # Get BART tokenizer tokenizer = llm.llm_engine.get_tokenizer_group() model="meta-llama/Llama-2-13b-chat-hf", speculative_model="ibm-fms/llama-13b-accelerator", # These are currently required for MLPSpeculator decoding use_v2_block_manager=True

flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 1, 5, 1.2, 0) # Hardware Accelerator Code import numpy as np frame1 = cv2.imread('./images/vtest/scene00002.png') prvs = cv2 Deploy  xDNN CNN Processing Engine > Highest Perf/Watt ➢ 4x higher real time inference over GPUs ➢ Easy to Use

flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 1, 5, 1.2, 0) # Hardware Accelerator Code import numpy as np frame1 = cv2.imread('./images/vtest/scene00002.png') prvs = cv2 Deploy  xDNN CNN Processing Engine