Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …

We present a new end-to-end generative adversarial network (GAN) for single image motion
deblurring, named DeblurGAN-V2, which considerably boosts state-of-the-art deblurring …

In natural language processing (NLP), enormous pre-trained models like BERT have
become the standard starting point for training on a range of downstream tasks, and similar …

(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical
subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve …

Experimental results based on offline processing reported at optical conferences
increasingly rely on neural network-based equalizers for accurate data recovery. However …

HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous
attention by automating the design of DNNs deployed in more resource-constrained daily …

In this paper, we provide a systematic approach for assessing and comparing the
computational complexity of neural network layers in digital signal processing. We provide …

Compressing convolutional neural networks (CNNs) has received ever-increasing research
focus. However, most existing CNN compression methods do not interpret their inherent …

Deep model compression has been extensively studied, and state-of-the-art methods can
now achieve high compression ratios with minimal accuracy loss. This paper studies model …