speedup Engineering intensiveMachine Learning based Program Optimizer TVM: Learning-based Learning System High-level data flow graph and optimizations Directly generate optimized program for new operator Runtimes NPUModule CUDAModule TFModule tvm::runtime::Module GetFunction(string) -> tvm::runtime::PackedFunc SaveToBinary/LoadFromBinary Runtime Module Interface SubclassesUnified Runtime Benefit mod. = tvm.module.load("mylib.so") func = lib["npufunction0"] func(a, b) Automatic RPC Support remote = tvm.rpc.connect(board_url, port) remote.upload("mylib.so") remote_mod = remote.load_module(“mylib

How Would That Look Like?© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM reserved. Option 2: Graph-Level Annotation ● Implement a Relay IR visitor to annotate a subgraph ● Module path: python/tvm/relay/op/contrib//graph_annotator.py ● Apply the annotator to Inc. or its Affiliates. All rights reserved. Partition the Relay IR graph ● No user involvement System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM

Prompting techniques 13 General prompting / zero shot 13 One-shot & few-shot 15 System, contextual and role prompting 18 System prompting 19 Role prompting 21 Contextual prompting 23 Table of contents February 2025 18 System, contextual and role prompting System, contextual and role prompting are all techniques used to guide how LLMs generate text, but they focus on different aspects: • System prompting sets and behavior. There can be considerable overlap between system, contextual, and role prompting. E.g. a prompt that assigns a role to the system, can also have a context. However, each type of prompt serves

reserved. Codegen for OpStrategy ● Each implementation defined will be compiled into a kernel in the module ● Dispatch logic will be compiled into another kernel as well # pseudocode for dispatch kernel as conv2d_NCHWc. Graph tuning is well defined for each subgraph. 3. Avoid runtime layout tracking system for operator requires layout transformation to optimize.© 2019, Amazon Web Services, Inc. or its

2017), where each Transformer block consists of an attention module and a Feed-Forward Network (FFN). However, for both the attention module and the FFN, we design and employ innovative archi- tectures n&=480/30=\boxed{16} \end{align*} Final Answer: The final answer is $16$. I hope it is correct. Problem: If the system of equations \begin{align*} 6x-4y&=a,\\ 6y-9x &=b. \end{align*}has a solution $(x, y)$ where $x$

Allocation – Linux Kernel Module DEPLOY VTA ON INTEL FPGA Setup Environment Variables Navigate to 3rdparty/cma and build kernel module Copy kernel module to DE10-Nano and Install Module CMA API Reference©2019 TVM with USE_VTA_FPGA flag ON Step 6: Copy the compiled TVM to the SDCard Step 7: Install kernel module cma.ko and run apps/vta_rpc/start_rpc_server.sh Step 8: Configure vta/config/de10nano_config.json

End >> 6 Relay (and NNVM) Graph Parser XIR Compiler Quantizer Partitioner @relay.transform.module_pass(opt_level=4) class AccelModule:© Copyright 2018 Xilinx TVM Partitioning >> 7 Subgraph 1 Parallel

更多 Deepseek 和 AI 资料，欢迎关注微信公众号【星禾光年 AI】，回复【deepseek】获取 1. 万能提示词生成模版：根据用户需求，帮助生成高质量提示词 SYSTEM 你是一位大模型提示词生成专家，请根据用户的需求编写一个智能助手的提示词，来指导大模型进行内容生成， 要求： 1. 以 Markdown 格式输出 2. 贴合用户需求，描述智能助手的定位、能力、知识储备 3 提示词应清晰、精确、易于理解，在保持质量的同时，尽可能简洁 4. 只输出提示词，不要输出多余解释 USER “ 请帮我生成一个 Linux ” 助手 的提示词 2. 文案大纲生成：根据用户提供的主题，来生成文案大纲 SYSTEM 你是一位文本大纲生成专家，擅长根据用户的需求创建一个有条理且易于扩展成完整文章的大纲，你拥有强大的 主题分析能力，能准确提取关键信息和核心要点。具备丰富的文案写作知识储备，熟悉各种文体和题材的文案大 创意性标题：为文章构思一个引人注目的标题，确保它既反映了文章的核心内容又能激发读者的好奇心。 USER “ ” 请帮我生成 中国农业情况 这篇文章的大纲 3. 中英翻译专家：中英文互译，对用户输入内容进行翻译 SYSTEM 你是一个中英文翻译专家，将用户输入的中文翻译成英文，或将用户输入的英文翻译成中文。对于非中文内容， 它将提供中文翻译结果。用户可以向助手发送需要翻译的内容，助手会回答相应的翻译结果，并确保符合中文语

multimodality across audio, visual, & text inputs 7/24: Apple releases Apple Intelligence, an AI system integrated into its devices, for developers 12/24: OpenAI announces o3, its highest-ever Unprecedented41 AI Performance = In 2024… Surpassed Human Levels of Accuracy & Realism, per Stanford HAI AI System Performance on MMLU Benchmark Test – 2019-2024, per Stanford HAI Note: The MMLU (Massive Multitask Human-Generated – 3/25, per Cameron Jones / Benjamin Bergen Date Released 5/24 1/25 2/25 AI system performance consistently improving over time AI Development Trending = Unprecedented43 AI Performance

complicated instructions 
 or consistently select incorrect tools, you may need to further divide your system and introduce more distinct agents. Practical guidelines for splitting agents include: Complex "Technical Support Agent", "You provide expert assistance with resolving technical issues, system outages, or product troubleshooting." "Sales Assistant Agent" "You help enterprise clients browse Guardrails Well-designed guardrails help you manage data privacy risks (for example, preventing system prompt leaks) or reputational risks (for example, enforcing brand aligned model behavior). 
 You