ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing

TL;DR

ElasticAI introduces a workflow with tools for automatically creating and deploying energy-efficient deep learning accelerators on embedded FPGAs, facilitating deployment in pervasive computing devices.

Contribution

The paper presents ElasticAI-Workflow, a novel system that automates the design and deployment of FPGA-based DL accelerators for embedded devices.

Findings

Successful automatic generation of FPGA accelerators

Verified performance on embedded FPGA platform

Potential for energy-efficient DL deployment in pervasive computing

Abstract

Deploying Deep Learning (DL) on embedded end devices is a scorching trend in pervasive computing. Since most Microcontrollers on embedded devices have limited computing power, it is necessary to add a DL accelerator. Embedded Field Programmable Gate Arrays (FPGAs) are suitable for deploying DL accelerators for embedded devices, but developing an energy-efficient DL accelerator on an FPGA is not easy. Therefore, we propose the ElasticAI-Workflow that aims to help DL developers to create and deploy DL models as hardware accelerators on embedded FPGAs. This workflow consists of two key components: the ElasticAI-Creator and the Elastic Node. The former is a toolchain for automatically generating DL accelerators on FPGAs. The latter is a hardware platform for verifying the performance of the generated accelerators. With this combination, the performance of the accelerator can be sufficiently guaranteed. We will demonstrate the potential of our approach through a case study.