In this article, we propose a lightweight and competitively accurate heart rhythm abnormality
classification model based on classical convolutional neural networks in deep neural …

Modern brain-computer interfaces and neural implants allow interaction between the tissue,
the user and the environment, where people suffer from neurodegenerative diseases or …

A serious challenge in artificial real-time applications is the hardware implementation of
deep neural networks (DNN). Among various methods, stochastic computing (SC)-based …

We argue that the vulnerability of model parameters is of crucial value to the study of model
robustness and generalization but little research has been devoted to understanding this …

We introduce QUANTISENC, a fully-configurable digital spiking neuromorphic hardware to
optimize performance and power consumption of spiking neural networks (SNNs) …

This paper investigates the performance of deep convolutional spiking neural networks
(DCSNNs) trained using spike-based backpropagation techniques. Specifically, the study …

Spiking neural networks (SNNs) are bioplausible machine learning models that use discrete
spikes to encode, compute, and transmit information. Combined with event-driven low …

Spiking neural network (SNN) is a neuromorphic system based on the information process
and store procedure of biological neurons. In this paper, a low-cost and high-speed …

Deep convolutional neural networks (DCNNs) have achieved state-of-the-art performance in
classification, natural language processing (NLP), and regression tasks. However, there is …

A detailed methodology for implementing a fully connected (FC) deep neural network (DNN)
and convolutional neural network (CNN) inference system on a field programming gate …