(Unfinished0822)Blog001 -190822 - TVM Series#003_TVM Introduction

TVM: An End to End IR Stack for Deploying Deep Learning Workloads on Hardware Platforms

• TVM：用于在硬件平台上部署深度学习工作负载的端到端IR堆栈

Tianqi Chen(project lead), Thierry Moreau(hardware stack), Ziheng Jiang†(graph compilation), Haichen Shen(gpu optimization)
Advisors: Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy
Paul G. Allen School of Computer Science & Engineering, University of Washington
DMLC open-source community
†Amazon Web Service

Deep learning has become ubiquitous and indispensable. Part of this revolution has been fueled by scalable deep learning systems, such as TensorFlow, MXNet, Caffe and PyTorch. Most existing systems are optimized for a narrow range of server-class GPUs, and require significant effort be deployed on other platforms such as mobile phones, IoT devices and specialized accelerators (FPGAs, ASICs). As the number of deep learning frameworks and hardware backends increase, we propose a unified intermediate representation (IR) stack that will close the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends.

• 深度学习已经变得无处不在，不可或缺。 这种革命的一部分是由可扩展的深度学习系统推动的，例如TensorFlow，MXNet，Caffe和PyTorch。 大多数现有系统针对窄范围的服务器级GPU进行了优化，并且需要在其他平台上部署大量工作，例如移动电话，物联网设备和专用加速器（FPGA，ASIC）。 随着深度学习框架和硬件后端数量的增加，我们提出了一个统一的中间表示（IR）堆栈，它将缩小以生产率为重点的深度学习框架与面向性能或效率的硬件后端之间的差距。