Contextual prompting 23 Table of contents Step-back prompting 25 Chain of Thought (CoT) 29 Self-consistency 32 Tree of Thoughts (ToT) 36 ReAct (reason & act) 37 Automatic Prompt Engineering 40 Code prompting 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 stealth and underwater exploration skills to survive. Table 9. An example of prompting for self consistency Yeah those topics seem like a good fit for a first-person video game. Let’s go back to the original

whether to drop tokens during inference according to the efficiency requirements, and always ensure consistency between training and inference. 3. Pre-Training 3.1. Experimental Setups 3.1.1. Data Construction

carried out in 3/25 using GPT-4.5. During the test, participants incorrectly identified the left image (Witness A) as human with 87% certainty, saying ‘A had human vibes. B had human imitation vibes B was human. AI Development Trending = Unprecedented44 AI Performance = Increasingly Realistic Image Generation… Notes: Dates shown are the release dates of each Midjourney model. Source: Midjourney Penguin, ‘How Midjourney Evolved Over Time (Comparing V1 to V6.1 Outputs)’ (9/24) AI-Generated Image: ‘Women’s Necklace with a Sunflower Pendant’ – 2/22-4/25, per Midjourney / Gold Penguin Model v1

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