context of natural language processing and LLMs, a prompt is an input provided to the model to generate a response or prediction. Prompt Engineering February 2025 8 These prompts can be used to achieve optimally for your task. Output length An important configuration setting is the number of tokens to generate in a response. Generating more tokens requires more computation from the LLM, leading to higher Putting it all together Choosing between top-K, top-P, temperature, and the number of tokens to generate, depends on the specific application and desired outcome, and the settings all impact one another

Development = Benefits & Risks The widely-discussed benefits and risks of AI – top-of-mind for many – generate warranted excitement and trepidation, further fueled by uncertainty over the rapid pace of change hardware) can do. Rather than executing pre-programmed tasks, AGI systems would understand goals, generate plans, and self-correct in real time. They could drive research, engineering, education, and logistics unprecedented rates. As noted on page 136, NVIDIA’s 2024 Blackwell GPU uses 105,000 times less energy to generate tokens than its 2014 Kepler predecessor. It’s a staggering leap, and it tells a deeper story –

Using TVM schedule primitive completely, no tensorize 。 Some Experience: 1 Avoid DataPack 2. Generate SMLAL instruction if your ARM does not have dot 3. compute_at is very important /NiiOS ! 驱动万物智能 /NiiOS ! 驱动万物智能 Alios TVM Q@ Hexagon DSP 。， Add Hexagon Code Generator inherits LLVM and could generate HVX instruction 。， Add one Hexagon runtimes named as libtvm_hexagon_runtime.so to support parallel next. We tvm.caLL_pure_intrin begin to do some work now. Such 本 站,可 as writing Tensorize to generate vec tvm,const(0， vrmpy instruction when we meet 人 ， GEM M. const(0，

operators or subgraphs 1. Implement extern operator functions, OR 2. Implement a graph annotator Generate binary/library/engine for the subgraph ● Implement an IR visitor for codegen ● Implement the build operators or subgraphs 1. Implement extern operator functions, OR 2. Implement a graph annotator Generate binary/library/engine for the subgraph ● Implement an IR visitor for codegen ● Implement the build

数'，指导在执行过程中如何分配计算资源。” 7. 适应提示：“在执行每个子任务后，评估其输出质量和对总体目标的 贡献，必要时调整后续任务的优先级或内容。” 思维拓展的认知理论基础 生成阶段（Generate）和探索阶段（Explore），可 以将这一理论应用到AI内容生成的过程中，设计相 应的提示语策略。 发散思维的提示语链设计 （基于“IDEA”框架） • Imagine（想象）：鼓励超越常规的思考 应用“多角度”提示探索不同视角 3. 使用“深化”提示拓展初始想法 4. 设计“反转”提示寻找替代方案 思维拓展的提示语链设计建立在创造性认知理论的基础上。根据Geneplore模型（Generate-Explore Model）， 创造性思维包括两个主要阶段： 思维拓展的提示语链设计 聚合思维的提示语链设计 基于“FOCUS”框架 • Filter（筛选）：评估和选择最佳想法 •

数'，指导在执行过程中如何分配计算资源。” 7. 适应提示：“在执行每个子任务后，评估其输出质量和对总体目标的 贡献，必要时调整后续任务的优先级或内容。” 思维拓展的认知理论基础 生成阶段（Generate）和探索阶段（Explore），可 以将这一理论应用到AI内容生成的过程中，设计相 应的提示语策略。 发散思维的提示语链设计 （基于“IDEA”框架） • Imagine（想象）：鼓励超越常规的思考 应用“多角度”提示探索不同视角 3. 使用“深化”提示拓展初始想法 4. 设计“反转”提示寻找替代方案 思维拓展的提示语链设计建立在创造性认知理论的基础上。根据Geneplore模型（Generate-Explore Model）， 创造性思维包括两个主要阶段： 思维拓展的提示语链设计 聚合思维的提示语链设计 基于“FOCUS”框架 • Filter（筛选）：评估和选择最佳想法 •

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Optimizer TVM: Learning-based Learning System High-level data flow graph and optimizations Directly generate optimized program for new operator workloads and hardware Hardware FrameworksWhy Automation

Quantize network test ‒ Test network accuracy finetune ‒ Finetune quantized network deploy ‒ Generate model for DPU ˃ Data Calibration data ‒ Quantize activation Training data ‒ Further increase

across the entire space (TensorCore + non-TensorCore) Our >olution 1 “Generate TensorCore code directly from Ta ea normal thread-level schedule