Design Considerations for Energy-efficient Inference on Edge Devices

Abstract

The emergence of low-power accelerators has enabled deep learning models to be executed on mobile or embedded edge devices without relying on cloud resources. The energy-constrained nature of these devices requires a judicious choice of a deep learning model and system configuration parameter to meet application needs while optimizing energy used during deep learning inference.