DeLight: Adding Energy Dimension To Deep Neural Networks

Abstract

Physical viability, in particular energy efficiency, is a key challenge in realizing the true potential of Deep Neural Networks (DNNs). In this paper, we aim to incorporate the energy dimension as a design parameter in the higher-level hierarchy of DNN training and execution to optimize for the energy resources and constraints. We use energy characterization to bound the network size in accordance to the pertinent physical resources. An automated customization methodology is proposed to adaptively conform the DNN configurations to the underlying hardware characteristics while minimally affecting the inference accuracy. The key to our approach is a new context and resource aware projection of data to a lower-dimensional embedding by which learning the correlation between data samples requires significantly smaller number of neurons. We leverage the performance gain achieved as a result of the data projection to enable the training of different DNN architectures which can be aggregated together to further boost the inference accuracy. Accompanying APIs are provided to facilitate rapid prototyping of an arbitrary DNN application customized to the underlying platform. Proof-of-concept evaluations for deployment of different visual, audio, and smart-sensing benchmarks demonstrate up to 100-fold energy improvement compared to the prior-art DL solutions.