Enabling Super-Fast Deep Learning on Tiny Energy-Harvesting IoT Devices

• URL: http://arxiv.org/abs/2111.14051v1
• Date: Sun, 28 Nov 2021 04:55:41 GMT
• Title: Enabling Super-Fast Deep Learning on Tiny Energy-Harvesting IoT Devices
• Authors: Sahidul Islam and Jieren Deng and Shanglin Zhou and Chen Pan and Caiwen Ding and Mimi Xie
• Abstract summary: Energy harvesting devices operate intermittently without batteries. implementing memory-intensive algorithms on EH devices is extremely difficult due to limited resources and intermittent power supply. This paper proposes a methodology that enables super-fast deep learning with low-energy accelerators for tiny energy harvesting devices.
• Score: 3.070669432211866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enabling sustainable smart applications. Nevertheless, implementing those computation and memory-intensive intelligent algorithms on EH devices is extremely difficult due to the challenges of limited resources and intermittent power supply that causes frequent failures. To address those challenges, this paper proposes a methodology that enables super-fast deep learning with low-energy accelerators for tiny energy harvesting devices. We first propose RAD, a resource-aware structured DNN training framework, which employs block circulant matrix with ADMM to achieve high compression and model quantization for leveraging the advantage of various vector operation accelerators. A DNN implementation method, ACE, is then proposed that employs low-energy accelerators to profit maximum performance with minor energy consumption. Finally, we further design FLEX, the system support for intermittent computation in energy harvesting situations. Experimental results from three different DNN models demonstrate that RAD, ACE, and FLEX can enable super-fast and correct inference on energy harvesting devices with up to 4.26X runtime reduction, up to 7.7X energy reduction with higher accuracy over the state-of-the-art.

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