contribute to TVML. ee Today we'ltouch on a few of those contribution areas: o Core Infrastructure Improvements to TVM o_uTVM: support for microcontrollers in TVM o_ Virtual Machine and dynamic NNs support High-Level 人 ORGREEE Te Conv2D mized RE -一 一 QQ octoML Transformer Improvements Transformer based models such as BERT have recently become very Popular and require first class 10 Virtual Machine e Many improvements from contributors at UW, AWS, and OctoML. e Initial implementation is quickly moving towards production quality. o _VM compiler VM runtime VM serialization

meticulously conduct quality filtering and proportion adjustments. We obtain code preference data based on compiler-feedback, and mathematical preference data based on the ground-truth labels. For reward model training DeepSeek-V2 Chat (RL) on standard benchmarks. Notably, DeepSeek-V2 Chat (SFT) demon- strates substantial improvements in GSM8K, MATH, and HumanEval evaluations compared with its base version. This progress can be

semi-borderless capital…all driving massive change. Sport provides a good analogy for AI’s constant improvements. As athletes continue to wow us and break records, their talent is increasingly enhanced by better 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 viable algorithms, based on how much computing power you'd need to reach top performance without any improvements. Source: Epoch AI (3/24) Impact of Improved Algorithms on AI Model Performance – 2014-2023, per

complex, interconnected workflows and systems. We’re seeing AI deliver significant, measurable improvements on three fronts: 01 Workforce performance Helping people deliver higher-quality outputs in shorter deployment to learn quickly from customer use cases and use that information to accelerate product improvements. That means shipping updates regularly, getting feedback, and improving performance and safety through iteration. The earlier you start, the more your organization benefits from compounding improvements. Klarna, a global payments network and shopping platform, introduced a new AI assistant to

Alibaba AL.Labs 阿里巴巴人工智能实验室 PowerVR GPU Alibaba Al.Labs 阿里巴巴人工智能实验室 PowerVR support by TVM NNVM Compiler -Execution graph -Model layers functions Computation Graph Optimizations -Param TvM Tensor Operators & Runtime Property Registr \L Compiler Toolchain 于 TVM TOPI Schedule Primitives & Optimizations Symbols NNVM & Param Frontends

Flow >> 5 MxNet CPU Layers FPGA Layers Runtime Image Model Weights Calibration Set Quantizer Compiler Tensor Graph Optimization Framework Tensor Graph to Xilinx Tensor Graph Frontend Deep Learning Copyright 2018 Xilinx TVM as Unified ML Front End >> 6 Relay (and NNVM) Graph Parser XIR Compiler Quantizer Partitioner @relay.transform.module_pass(opt_level=4) class AccelModule:© Copyright 2018

Services, Inc. or its Affiliates. All rights reserved. Let TVM Be the Compiler of Your Chip Your chip can run any models Your compiler (TVM) supports multiple frontends (e.g., TensorFlow, PyTorch, MXNet)

a success message print("Files renamed successfully.") ``` Additionally, there are a few other improvements that can be made to the code: 1. The file extension of the new filenames is not kept. It’s better the file {file}: {e}") # Print a success message print("Files renamed successfully.") ``` These improvements make the code more robust and flexible while also making it easier to read and understand...

al. IEEE Micro 2019. VTA Hardware/Software Interface (ISA) VTA MicroArchitecture VTA Simulator} compiler, driver, hardware design full stack open source Current TVM Stack VTA Runtime & JIT CompilerTSIM:

Model Analysis Graph optimization Blade Graph Optimizer TensorRT Customized OptimizeT TAO Compiler (XLA) cuUBLAS/VcuDNNVCUTL， Blade Kernel Lib S， ation 计算平台事业部 COMPUTING PLATFORM Weight Adjustment