… to improve energy efficiency. This research focuses on different design layers of deeplearning acceleration systems. First, the algorithm characteristics of deeplearning network models …
… these module functions according to the module and data dependency, while retaining the hardware utilization. Such a process is referred to as dynamic pipelining in this study. …
… convolutional neural network in currentarchitecture. In addition, for some small, low-power hardware devices, embedded processors are almost the same in computing architecture as …
… In this paper, we explain the integrated communication and localization in mmWave systems, in which these processes share the same set of hardwarearchitecture and algorithms. We …
… and acceleratingdeep neural network … AI hardware manufacturers, model quantization has emerged as a promising approach for the compression and acceleration of machine learning …
… for designing efficienthardware accelerators: the large number of computational primitives and irregular control flows. To address these two challenges, we propose a hardware …
… of this study is to examine the success of EfficientNet-B0 deeplearningarchitecture in the … In this work, the lightweight EfficientNet-B0 deeplearningarchitecture was proposed and …
… Finally, neural networks are easily parallelized, allowing them to take advantage of modern hardwarearchitectures such as graphical processing units and tensor processing units. With …
… The architecture quick update of computing platforms and … suitable hardware resources for acceleratingdeep neural network… mapped to parallel hardwarearchitectures and that facilitate …