The Chinese Communist Party’s concept of security is expanding to meet new systemic
challenges to continued one-party rule. Threats such as ideological change and financial …

… to improve energy efficiency. This research focuses on different design layers of deep learning
acceleration systems. First, the algorithm characteristics of deep learning 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 current architecture. 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 hardware architecture and algorithms. We …

… and accelerating deep neural network … AI hardware manufacturers, model quantization has
emerged as a promising approach for the compression and acceleration of machine learning …

… for designing efficient hardware 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 deep learning architecture in the …
In this work, the lightweight EfficientNet-B0 deep learning architecture was proposed and …

… Finally, neural networks are easily parallelized, allowing them to take advantage of modern
hardware architectures such as graphical processing units and tensor processing units. With …

… The architecture quick update of computing platforms and … suitable hardware resources for
accelerating deep neural network… mapped to parallel hardware architectures and that facilitate …