learning.TVM Stack High-Level Differentiable IR Tensor Expression and Optimization Search Space LLVM, CUDA, Metal VTA Edge FPGA Cloud FPGA ASIC Optimization AutoTVM Device FleetExisting Deep Learning Design in Verilog VerilatorToward Unified IR InfraOverview of New IR Infra Single unified module/pass, type system, with function variants supportCompilation Flow under the New Infra IRModule (relay::Function) print(mod[”te_add_one”].args) Use hybrid script as an alternative text format Directly write pass, manipulate IR structures Accelerate innovation, 
 e.g. use (GA/RL/BayesOpt/your favorite ML method)

written in TVM Tensor IR .. More targets AutoTVM – Tuning the kernels Optimized Binary Codegen – LLVM, Cuda, C, … Framework Parsers Graph level optimizations Tensor-level optimizations Machine code operators • We introduced a new Relay dialect – QNN to encapsulate this work • Complete reuse of Relay pass infrastructure • Possible reuse of TVM schedules (only to some extent)© 2019, Amazon Web Services

System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter) System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM, CUDA, Metal, VTA Serialized Subgraph Library Relay Runtime (VM, Graph Runtime, Interpreter)

835), mate10/mate10pro (kirin 970), p20/p20pro (kirin 970) -target=arm64-linux-android -mattr=+neon llvm firefly rk3399, rock960, ultra96 -target=aarch64-linux-gnu -mattr=+neon rasp3b (bcm2837) -targ (mali g71) N/A FPGA vta pynq, ultra96 N/A sdaccel Out-of-tree support or WIP: Hexagon DSP (via llvm), Ascend NPU, and more Green: Linaro 96BoardsLinaro for TVM ● Linaro AI/ML group can be a good fit

pointwise convolution we implement im2col schedule 。 No tensorize, but in schedule to cooperate with LLVM to simulate GEMM microkernel /NiiOS ! 驱动万物智能 Alios TVM @ ARM CPU FP32 Performance Comparison AARCH64 DSP Processor /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

“-shared”, “-fPIC”, “-m32”] b. python tensorflow_blur.py to get the .log c. Use the .log, with target=“llvm –mcpu=i686 –mtriple=i686-linux-gnu” then TVM_NDK_CC=“clang –m32” python tf_blur.py���������������

2019·11·16绝赞招聘中 TVM 在平头哥 • 工具链产品 平头哥芯片平台发布的配套软件中， TVM 是工具链产品的重要组成部分： 负责将预训练好的 caffe 或者 tensorflow 的模型，转换到 LLVM IR，最后生成可以在无剑 SoC 平台上 执行的二进制。绝赞招聘中 为何添加 caffe 前端？ 客户需求 评估阶段：客户用于评估芯片的网络，caffe 模型占很大比重。 竞品已支持

VMCompiler() with tvm.autotvm.apply_graph_best("resnet50_v1_graph_opt.log"): vm = vmc.compile(mod, "llvm") vm.init(ctx) vm.load_params(params) data = np.random.uniform(size=(1,

40.2 38.7 AGIEval (Acc.) 0-shot 41.3 64.4 43.4 49.8 51.2 Code HumanEval (Pass@1) 0-shot 45.1 43.9 53.1 48.2 48.8 MBPP (Pass@1) 3-shot 57.4 53.6 64.2 68.6 66.6 CRUXEval-I (Acc.) 2-shot 42.5 44.3 52.4 arXiv:2206.07682, 2022. T. Wei, J. Luan, W. Liu, S. Dong, and B. Wang. Cmath: Can your language model pass chinese elementary school math test?, 2023. L. Xu, H. Hu, X. Zhang, L. Li, C. Cao, Y. Li, Y. Xu, figure, DeepSeek-V2 Chat (RL) demonstrates considerable proficiency in LiveCodeBench, achieving a Pass@1 score that even surpasses some giant models. This performance highlights the strong capability of

任务上，性能比肩OpenAl-o1正式版。 (Pass@1) (Percentile) (Pass@1) (Pass@1) (Pass@1) 国产 十 免费 十 开源 十 强大 Accuracy/Percent le (%) AI https://chat.deepseek.com Z u N e P 6 7 K w S v L C q Y 4 Y V 1 T 8 0 u m B k k m O x