An Energy-Efficient Method for Recurrent Neural Network Inference in Edge Cloud Computing

Recurrent neural networks (RNNs) are widely used to process sequence-related tasks such as natural language processing. Edge cloud computing systems in an asymmetric structure, where task managers allocate the edge and based on computation requirements. In a system, servers have no energy limitations, since they unlimited resources. systems, however, resource-constrained, consumption is thus expensive, which requires energy-efficient method for RNN job this paper, we propose low-overhead, energy-aware runtime manager computing. The latency defined quality of service (QoS) requirement. Based QoS requirements, dynamically assigns inference performs optimization using dynamic voltage frequency scaling (DVFS) techniques. Experimental results real system indicate that our can reduce up 45% compared with state-of-the-art approach.