Enabling Fast Deep Learning on Tiny Energy-Harvesting IoT Devices
Sahidul Islam1, Jieren Deng2, Shanglin Zhou2, Chen Pan3, Caiwen Ding2, Mimi Xie1
1Department of Computer Science, The University of Texas at San Antonio
2Department of Computer Science, University of Connecticut
3Department of Computer Science, Texas A&M University–Corpus Christi
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 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 and structured pruning to achieve high compression 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 small 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 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.
Keywords: Deep Learning, Low-energy Accelerator, Energy Harvesting, IoT.
