support for microcontrollers in TVM o_ Virtual Machine and dynamic NNs support (w/ AWS folks) o_ Improved NLP support, with focus on transformers QQ octoML Core Infrastructure Refactors ee New Integer very Popular and require first class support in TVML. ee What we've done: o Extend the relay ONNX frontend to support all opset versions of BERT. 里This enables importing of native ONNX models and those converted Tensorflow. 5 ， Improve scheduling of batch matrix multiplies. 时”Early autotuning templates improve performance by ~20% e What we're working on: This prevents most compute layers from being fused. Reshape

= Unprecedented • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising • AI Usage + Cost + Loss Growth = Unprecedented • AI Monetization China USA – LLM #2 AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising 3 Cost of Key Technologies Relative to Launch Year % of competitive. Breakthroughs in large models, cost-per-token declines, open-source proliferation and chip performance improvements are making new tech advances increasingly more powerful, accessible, and economically

Quantizer Compiler Tensor Graph Optimization Framework Tensor Graph to Xilinx Tensor Graph Frontend Deep Learning Frameworks https://github.com/xilinx© Copyright 2018 Xilinx TVM as Unified ML Front c_char_p(graph_path.value).value layout = c_char_p(output_layout.value).value … >> 12© Copyright 2018 Xilinx Performance Pipelines ˃ References to our latest results: https://github.com/Xilinx/AI-Model-Zoo (embedded measurements we track: Latency & Throughput ˃ ML pipeline contains multiple stages, performance limited by slowest one ˃ Performance results based on Xilinx own runtime pipeline available in github (https://github

int32] */} Asymmetric© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Frontend Parsers • TFLite Pre-quantized Models • In good shape • Supports all Image Classification PreQuantized reserved. Accuracy© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Performance Comparison • Metric – Latency in ms for batch size = 1 • 1.7x speedup on Inception asymmetric

through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models. The model checkpoints are available at h t t p s : / / g i t h u b . p S e e k - V 2 . 0 20 40 60 80 100 Activated Parameters (Billions) 55 60 65 70 75 80 Performance (MMLU) DeepSeek-V2 DeepSeek 67B LLaMA 1 33B LLaMA 1 65B LLaMA 2 13B LLaMA 2 34B LLaMA 2

We’re seeing AI deliver significant, measurable improvements on three fronts: 01 Workforce performance Helping people deliver higher-quality outputs in shorter 
 time frames. 02 Automating routine product improvements. That means shipping updates regularly, getting feedback, and improving performance and safety at every step. The result: users access new advancements in AI early and often—and job matching engine against the GPT-powered version with the new, customized context. 
 The performance uplift was significant: A 20% increase in job applications started A 13% uplift in downstream

well is to build your agent prototype with the most capable model for every task to establish a performance baseline. From there, try swapping in smaller models to see 
 if they still achieve acceptable fail. In summary, the principles for choosing a model are simple: 01 Set up evals to establish a performance baseline 02 Focus on meeting your accuracy target with the best models available 03 Optimize for many complex workflows, splitting up prompts and tools across multiple agents allows for improved performance and scalability. When your agents fail to follow complicated instructions 
 or consistently

Engineering February 2025 25 Step-back prompting Step-back8 prompting is a technique for improving the performance by prompting the LLM to first consider a general question related to the specific task at hand thought appears to improve robustness when moving between different LLM versions. Which means the performance of your prompt should drift less between different LLMs than if your prompt does not use reasoning (APE). This method15 not only alleviates the need for human input but also enhances the model’s performance in various tasks. You will prompt a model to generate more prompts. Evaluate them, possibly alter

224] dtype = "float32" block = get_model('resnet50_v1', pretrained=True) mod, params = relay.frontend.from_mxnet(block, shape={input_name: input_shape}, dtype=dtype) tvm.relay.transform.dispatch_global_func(mod

1.31 -35 1 129 中131 124有23152136 2 1.14 am omo oo Convolution Workload Performance AiOS 1驱动万物智能 Alios TVM @ ARM CPU INT8 Depthwise Convolution 。， NHWC layout 。 Using TVM schedule 09工08 工区 0.77 0.77 | | | Depthwise Convolution Workload Performance Alios TVM @ ARM CPU INT8 Performance Comparison @ rasp 3b+ AARCH64 aoo0 8.87 sm ao 7m am sm 3.83 om ao 2.08 2 to cooperate with LLVM to simulate GEMM microkernel /NiiOS ! 驱动万物智能 Alios TVM @ ARM CPU FP32 Performance Comparison AARCH64 12 135 117 工1 1 1.07 国