improve on the points as we all aim to adapt to this evolving journey as knowledge – and its distribution – get leveled up rapidly in new ways. Special thanks to Grant Watson and Keeyan Sanjasaz and Momentum22 Knowledge Distribution Evolution = Over ~Six Centuries23 Knowledge Distribution – 1440-1992 = Static + Physical Delivery… Source: Wikimedia Commons Knowledge Distribution Evolution = Over ~Six ~Six Centuries Printing Press – Invented 144024 …Knowledge Distribution – 1993-2021 = Active + Digital Delivery… *The internet is widely agreed to have been ‘publicly released’ in 1993 with release

language model input and output, a text prompt (sometimes accompanied by other modalities such as image prompts) is the input the model uses to predict a specific output. You don’t need to be a data scientist generated text. • Top-K sampling selects the top K most likely tokens from the model’s predicted distribution. The higher top-K, the more creative and varied the model’s output; the lower top-K, the more

We evaluate the generation throughput of DeepSeek-V2 based on the prompt and generation length distribution from the actually deployed DeepSeek 67B service. On a single node with 8 H800 GPUs, DeepSeek-V2

Overlay Processor ˃ DNN Specific Instruction Set Convolution, Max Pool etc. ˃ Any Network, Any Image Size ˃ High Frequency & High Compute Efficiency ˃ Supported on U200 – 3 Instances U250 – 4 Instances Systolic Array Bias ReLU Bias ReLU Bias ReLU Bias ReLU Pooling Pooling Pooling Pooling Image Queue Instruction Buffer Cross Bar Pooling/ EWA© Copyright 2018 Xilinx Xilinx Edge DPU IP (DPUv2) networks >> 4© Copyright 2018 Xilinx Inference Flow >> 5 MxNet CPU Layers FPGA Layers Runtime Image Model Weights Calibration Set Quantizer Compiler Tensor Graph Optimization Framework Tensor Graph

码、运行时、系统工具、系统库和设置。 2. 镜像（Image）：用于创建容器的只读模板。一个镜像可以包含完整的操作 系统环境。 3. Dockerfile：定义镜像内容的文本文件，包含了构建镜像的所有指令。 4. Docker Hub：公共的 Docker 镜像仓库，用于存储和分发 Docker 镜像。 5. 拉取镜像：docker pull <image_name> 6. 构建镜像：在包含 Dockerfile Dockerfile 目录中运行：docker build -t <image_name> . 常用命令： 1. 列出正在运行的容器：docker ps 2. 列出所有容器：docker ps -a 3. 停止一个容器：docker stop 4. 删除一个容器：docker rm 20 4.2.2 下载 docker docker

requires 40us sampling net runtime - First PyTorch model used a 3,400us sampling net runtime Image from LPCNetExit, Pursued By A Bear - 3400us (baseline), 40us (target) - 85x speedup - Uh ohEnter general technique, allows clean vectorization - Related work in Gibiansky (2017), Gray (2019), et al. Image from OpenAI- Add relay.nn.sparse_dense for block-sparse matrix multiplication (~50 lines of TVM IR)

SDCard Image from Terasic (Require Registration) Step 3: Get files from https://github.com/liangfu/de10-nano-supplement Step 4: Extract the files Step 4.1: Replace the zImage in SDCard Image Step 4

rights reserved. Frontend Parsers • TFLite Pre-quantized Models • In good shape • Supports all Image Classification PreQuantized hosted models • MXNet Pre-quantized Models • Tested internally with

Models with dynamism ● Control flow (if, loop, etc) ● Dynamic shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control