Growth Over Nine Years of… Compute Gains from Better Algorithms Led To… Note: Estimates how much progress comes from bigger models versus smarter algorithms, based on how much computing power you'd need to to reach top performance without any improvements. Source: Epoch AI (3/24) Impact of Improved Algorithms on AI Model Performance – 2014-2023, per Epoch AI Effective Compute (Relative to 2014) +200% …AI’s power demands are increasing – and its progress is increasingly bottlenecked not by data or algorithms, but by the grid and strains related to demand. While AI presently places considerable demands

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) Mark supported operators or subgraphs 1. Implement an operator-level annotator, OR 2. Implement a graph-level annotator© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Option 1: rights reserved. Option 2: Graph-Level Annotation ● Implement a Relay IR visitor to annotate a subgraph ● Module path: python/tvm/relay/op/contrib//graph_annotator.py ● Apply the annotator

ingests a FP32 graph and a small dataset • Finds suitable quantization scale • Produces a quantized graph • Compiling Pre-quantized models – QNN Dialect • TVM ingests a pre-quantized graph in TFLite or Affiliates. All rights reserved. TVM Overview Framework Graph Mxnet TF …. parsers Relay Graph Target-independent Relay passes Target-optimized graph Target-dependent Relay passes Intel x86 ARM CPU targets AutoTVM – Tuning the kernels Optimized Binary Codegen – LLVM, Cuda, C, … Framework Parsers Graph level optimizations Tensor-level optimizations Machine code generation© 2019, Amazon Web Services

Runtime Image Model Weights Calibration Set Quantizer Compiler Tensor Graph Optimization Framework Tensor Graph to Xilinx Tensor Graph Frontend Deep Learning Frameworks https://github.com/xilinx© Copyright 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 supported/not supported, pattern matching graph colorization - Choices how to partition especially for multi-branch networks (i.e. YOLOv3, SSD)© Copyright 2018 Xilinx TVM Graph Partitioning/Fusion >> 8 Subgraph

dependent: arange, nms, etc. ○ Control flow: concatenate within a while loop Limitation of TVM/graph runtime ● Cannot compile and run dynamic models© 2019, Amazon Web Services, Inc. or its Affiliates new runtime for Relay ● Dynamic codegen (WIP) ○ Kernel dispatch for a single op ○ Graph dispatch for a (sub-)graph In collaboration with Jared Roesch, Zhi Chen, Wei Chen© 2019, Amazon Web Services, implement© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Dispatch a Whole Graph Resnet Data -> (Any, 3, 224, 224) Dispatch Tree Resnet_copy0 Resnet_copy1 ... 1 <= bs < 17 17

parallel all-to-all communication. We also customize faster CUDA kernels for communications, routing algorithms, and fused 12 1K 12K 24K 35K 47K 58K 70K 81K 93K 104K 116K 128K Context Length (#Tokens) 0

reserved. Amazon Trademark Deploy GluonCV Models GluonCV Models MXNet Computational Graph Json Acyclic Graph Export As-is Optimize with TVM© 2019, Amazon Web Services, Inc. or its Affiliates. All

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 &

ASIC Optimization AutoTVM Device FleetExisting Deep Learning Frameworks High-level data flow graph Hardware Primitive Tensor operators such as Conv2D eg. cuDNN Offload to heavily optimized intensiveMachine Learning based Program Optimizer TVM: Learning-based Learning System High-level data flow graph and optimizations Directly generate optimized program for new operator workloads and hardware

PLATFORM COMPUTING PLATFORM INT8 Inference on PAI- 引FTe[= PAI-Blade Model Analysis Graph optimization Blade Graph Optimizer TensorRT Customized OptimizeT TAO Compiler (XLA) cuUBLAS/VcuDNNVCUTL， Blade