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 truncating division. e Unified Object and Node system for TVM runtime o Lays groundwork forimproved multi-language support for expPosing runtime, and |IRs. QQ octoML Unified Object Protocol vm::Object NDArray | Rd | tuplelclosure AST Nodes Cross language suppPort Easy to introduce new runtime objects (trees, graphs) Direct access from other languages QQ octoML HTVM Overview *。 Plug directly

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) Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Target Device General Devices (CPU/GPU/FPGA) Mark supported Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) Your Dispatcher Target Device General Devices (CPU/GPU/FPGA) Mark supported

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 to Xilinx Tensor 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 11 Calls XDNN’s TVM registered function to access the FPGA runtime APIs© Copyright 2018 Xilinx Registering TVM op in Python at runtime File contrib_xlnx.py: … @tvm.register_func("tvm.accel.accel_fused")

goingUnified Runtime For Heterogeneous Devices CUDA Driver NPU Driver Device Drivers External Runtimes NPUModule CUDAModule TFModule tvm::runtime::Module GetFunction(string) -> tvm::runtime::PackedFunc nc SaveToBinary/LoadFromBinary Runtime Module Interface SubclassesUnified Runtime Benefit mod.export_library("mylib.so") Unified library packaging Free API (Py/Java/Go) lib = tvm.module.load("mylib remote_b)Virtual Machine: Supporting Dynamic Workload Dynamic shape workloads More runtime objects: Arrays, Tuples, Trees, ADTs Minimum runtime for dynamic models Credit: Jared Roesch, Haichen Shen et.aluTVM: TVM

by TVM NNVM Compiler -Execution graph -Model layers functions Computation Graph Optimizations -Param TvM Tensor Operators & Runtime Property Registr Registr \L Compiler Toolchain 于 TVM TOPI Schedule Primitives & Optimizations Symbols NNVM & Param Frontends Operators Algorithm &Schedule CUDA TOPI Backends Machine Learning Automated Optimizer

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

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...

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