… However, our work considers the DNN inference services, in which inference accuracy is a
… management and provide high-accuracy inference services for intelligent IIoT applications. …

… -intensive deep neural network (DNN) inference services, … In this article, we investigate the
collaborative DNN inference … Specifically, sampling rate adaption, inference task offloading…

… allocation for deep neural network (DNN) inference in the … DNN inference tasks, a resource
management problem is formulated with the objective of maximizing the average inference …

… Deep RL platform, called FA3C. Traditionally, FPGA-based DNN accelerators have
mainly focused on inference … Our platform targets both inference and training using single-precision …

… algorithm locally and get an inference scheme based on local … In this paper, we investigate
DNN inference acceleration in … First, we formulate task offloading of DNN inference among …

… CNNs’ inference latency may become a bottleneck for Deep Learning adoption by … -DNN,
a fully automatic search based on Reinforcement Learning which, combined with an inference …

… Therefore, AutoScale leverages a reinforcement learning … to maximize the DNN inference
energy efficiency while … an in-depth characterization of DNN inference execution on mobile and …

… We focus on deep neural network (DNN) based classification … level and delay performance
of DNN inference via device-edge … DNN inference in industrial IoT via deep reinforcement …

… We also propose an automated quantization flow powered by deep reinforcement learning
to search for the best quantization configuration in the large design space. Our evaluation …

… In order to mitigate the potential oscillation in the DNN inference results, we adopt the duplicate
Q method from [15], which maintains two Q value estimates for each state-action pair and …