The staggering innovations and emergence of numerous deep learning (DL) applications
have forced researchers to reconsider hardware architecture to accommodate fast and …

In this paper, we present a novel approximate computing scheme suitable for realizing the
energy-efficient multiply-accumulate (MAC) processing. In contrast to the prior works that …

Real-time coal mine intelligent monitoring for pedestrian identifying and positioning is an
important means to ensure safety in production. Traditional object detection models based …

This paper presents an approximate computing method of long short-term memory (LSTM)
operations for energy-efficient end-to-end speech recognition. We newly introduce the …

The recognition performance of convolutional neural networks has surpassed that of
humans in many computer vision areas. However, there is a large number of parameter …

Agriculture holds a crucial position in maintaining livelihoods and securing food sources,
particularly in nations such as Ethiopia, where a substantial portion of the population …

Some applications have the property of being resilient, meaning that they are robust to noise
(eg due to error) in the data. This characteristic is very useful in situations where an …

In this paper, we propose a novel end-to-end stress recognition model by combining
binarized convolutional neural network (CNN) and long short-term memory (LSTM) models …

Recently, deep learning and convolutional neural networks (CNNs) have reported several
promising results in the classification of Motor Imagery (MI) using Electroencephalography …

Based on recent RISC-V designs, we present in this paper a low-power vector processor
architecture for efficiently deploying vision transformer (ViT) models. To fairly measure the …