Autotile: Autonomous Task-tiling for Deep Inference on Battery-less Embedded System
Jishnu Banerjee, Sahidul Islam, Wei Wei, Pan Chen, Mimi Xie · 2024
Deep Neural Networks (DNNs) are increasingly applied in various intelligent applications for enhanced accuracy for in-situ decision-making. Considering the cost and longevity, those intelligent applications usually employ energy harvesting (EH) for power supply. Nevertheless, due to inherent intermittency, EH power can frequently disrupt the runtime operation, resulting in subsequent forward progress loss when executing long computations of DNN inference. To address this issue, DNN tiling has been employed where the input data is partitioned into multiple smaller tiles for efficient runtime operation. However, under the energy harvesting scenarios, the size of the tiles can influence runtime energy efficiency significantly under different EH conditions. Therefore, we proposed environmentally adaptive dynamic DNN tiling methods to optimize energy efficiency and runtime reliability. The experimental results on a real testbed show that the proposed technique can outperform the state-of-the-art methods by 19.24% on average.