ORNL researchers design novel method for energy-efficient deep neural networks

March 14, 2018 – An Oak Ridge National Laboratory method to improve the energy efficiency of scientific artificial intelligence is showing early promise in efforts to parse insights from volumes of cancer data. Researchers are realizing the potential of deep learning to rapidly advance science, but "training" the underlying neural networks with large volumes of data to tackle the task at hand can require large amounts of energy. These networks also require complex connectivity and enormous amounts of storage, both of which further reduce their energy efficiency and potential in real-world applications. To address this issue, ORNL's Mohammed Alawad, Hong-Jun Yoon, and Georgia Tourassi developed a novel method for the development of energy-efficient deep neural networks capable of solving complex science problems. They presented their research at the 2017 IEEE Conference on Big Data in Boston. The researchers demonstrated that by converting deep learning neural networks (DNNs) to "deep spiking" neural networks (DSNNs), they can improve the energy efficiency of network design and realization.