Microsoft, Google, Anthropic, Meta, Apple, Alibaba, Deepseek, UK Government, US Department of Homeland Security. China data may be subject to informational limitations due to government restrictions. 3/23: AI assistant focused on safety & inter- pretability 3/24: USA Department of Homeland Security unveils its AI Roadmap Strategy 5/24: OpenAI releases GPT-4o, which has full multimodality 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

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 factors, the email should be classified as **IMPORTANT**. The bug in the contact form is a serious security risk, and the sender’s credibility is unknown. The recipient should take immediate action to fix

They decided to get AI into the hands of employees—working closely with Legal, Compliance, and IT Security teams to ensure responsible use. They rolled out ChatGPT Enterprise globally, then let people discover now build consistently 
 high-quality apps, faster, without having to get into the source code. Security, guardrails, and routing logic are all built in. 18 AI in the EnterpriseAs a result, AI app development the EnterpriseThe trusted AI enterprise platform Security and privacy at a glance For our enterprise customers, nothing is more important than security, privacy and control. 
 Here’s how we ensure it:

extensive and intricate rulesets, making updates costly or error-prone, 
 for example performing vendor security reviews. 03 Heavy reliance on unstructured data: Scenarios that involve interpreting natural language robust authentication and authorization protocols, strict access controls, and standard software security measures. 24 A practical guide to building agents Think of guardrails as a layered defense mechanism Add new guardrails based on real-world edge cases and failures you encounter 03 Optimize for both security and user experience, tweaking your guardrails as your
 agent evolves. 27 A practical guide to building

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