Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Model compression is an important technique to facilitate efficient embedded and hardware
implementations of deep neural networks (DNNs), a number of prior works are dedicated to …

Deep Neural Networks (DNNs) have been widely used in various artificial intelligence (AI)
applications due to their overwhelming performance. Furthermore, recently, several …

Recently, deep neural network (DNN) hardware accelerators have been reported for energy-
efficient deep learning (DL) acceleration [1–6]. Most prior DNN inference accelerators are …

The breakthrough of deep learning has started a technological revolution in various areas
such as object identification, image/video recognition and semantic segmentation. Neural …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

Custom accelerators improve the energy efficiency, area efficiency, and performance of
deep neural network (DNN) inference. This article presents a scalable DNN accelerator …

Large deep neural network (DNN) models pose the key challenge to energy efficiency due
to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or …

Recent developments in deep neural network (DNN) pruning introduces data sparsity to
enable deep learning applications to run more efficiently on resourceand energy …

This chapter presents HNPU, which is an energy-efficient DNN training processor by
adopting algorithm–hardware co-design. The HNPU supports stochastic dynamic fixed-point …