In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …

This paper proposes energy-efficient approximate multipliers based on the Mitchell's log
multiplication, optimized for performing inferences on convolutional neural networks (CNN) …

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

This paper presents a simulated memristor crossbar based Convolutional Neural Network
(CNN). Deep networks implemented on GPU clusters have become the state of the art in …

Memristor-based hardware accelerators are gaining an increased attention as a potential
candidate to speed-up the vector-matrix operations commonly needed in many digital image …

This paper describes a memristor-based neuromorphic system that can be used for ex situ
training of various multi-layer neural network algorithms. This system is based on an …

Time-domain computing (TC) has drawn significant attention recently due to its highly
efficient computation for applications such as image processing and neural network …

This paper describes a memristor-based neuromorphic system that can be used for ex-situ
training of various multi-layer neural network algorithms. This system is based on an analog …

In this paper, a novel RRAM-based reconfigurable accelerator (R-accelerator) design is
proposed, which makes special use of existing RRAM device for high-efficient …

Mixed-signal time-domain computing (TC) has recently drawn significant attention due to its
high efficiency in applications such as machine learning accelerators. However, due to the …