Data redundancy is ubiquitous in the inputs and intermediate results of Deep Neural
Networks (DNN). It offers many significant opportunities for improving DNN performance and …

Processing In-Memory (PIM) has shown a great potential to accelerate inference tasks of
Convolutional Neural Network (CNN). However, existing PIM architectures do not support …

Convolutional neural networks (CNNs) are used in our daily life, including self-driving cars,
virtual assistants, social network services, healthcare services, and face recognition, among …

This article analyzes the effects of approximate multiplication when performing inferences on
deep convolutional neural networks (CNNs). The approximate multiplication can reduce the …

Hyperdimensional (HD) computing is a novel computational paradigm that emulates the
brain functionality in performing cognitive tasks. The underlying computation of HD involves …

In this article, a technique, based on using Residue Number System (RNS) is suggested to
improve the energy efficiency of Deep Neural Networks (DNNs). In the DNN architecture …

As the size of data generated every day grows dramatically, the computational bottleneck of
computer systems has been shifted toward the storage devices. Thanks to recent …

Analog computing has reemerged as a promising avenue for accelerating deep neural
networks (DNNs) to overcome the scalability challenges posed by traditional digital …

Arithmetic plays a major role in a computer? s performance and efficiency. Building new
computing platforms supported by the traditional binary arithmetic and silicon-based …

Brain-inspired hyperdimensional (HD) computing emulates cognition by computing with
long-size vectors. HD computing consists of two main modules: encoder and associative …