ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing
This addresses the problem of energy-efficient deep learning deployment for pervasive computing applications, but it appears incremental as it builds on existing FPGA-based accelerator methods.
The paper tackles the challenge of deploying deep learning on embedded devices with limited computing power by proposing ElasticAI-Workflow, which helps developers create and deploy energy-efficient DL accelerators on embedded FPGAs, demonstrating its potential through a case study.
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.