TVM 各个模块总体架构

Deploy Deep Learning Everywhere

Existing Deep Learning Frameworks

Limitations of Existing Approach

Learning-based Learning System

Problem Setting

Example Instance in a Search Space

Optimization Choices in a Search Space

Problem Formalization

Black-box Optimization

Cost-model Driven Approach

Statistical Cost Model

Unique Problem Characteristics

Vanilla Cost Modeling

Program-aware Modeling: Tree-based Approach

Program-aware Modeling: Neural Approach

Comparisons of Models

Unique Problem Characteristics

Transferable Cost Model

Impact of Transfer Learning

Learning to Optimize Tensor Programs

Device Fleet: Distributed Test Bed for AutoTVM

TVM: End to End Deep Learning Compiler

Tensor Expression and Optimization Search Space

Search Space for CPUs

Hardware-aware Search Space

Search Space for GPUs

Search Space for TPU-like Specialized Accelerators

Tensorization Challenge

Search Space for TPU-like Specialized Accelerators

Software Support for Latency Hiding

Summary: Hardware-aware Search Space

VTA: Open & Flexible Deep Learning Accelerator

TVM: End to End Deep Learning Compiler

Need for More Dynamism

Relay Virtual Machine

uTVM: TVM on bare-metal Devices

Core Infrastructure

TSIM: Support for Future Hardware

Unified Runtime For Heterogeneous Devices

Unified Runtime Benefit

Effectiveness of ML based Model

Comparisons of Models

Device Fleet in Action

End to End Inference Performance (Nvidia Titan X)

Portable Performance Across Hardware Platforms

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