Many IoT applications require the use of multiple deep neural networks (DNNs) to perform
various tasks on low-cost edge devices with limited computation resources. However …

We propose a novel synaptic architecture based on a NAND flash memory for highly robust
and high-density quantized neural networks (QNN) with 4-bit weight and binary neuron …

With the increasing urban population, city expan-sions come along with the problem of traffic
management. In today's era of the computational world, where there is a smart AI-based …

Binary neural networks (BNNs) substitute complex arithmetic operations with simple bit-wise
operations. The binarized weights and activations in BNNs can drastically reduce memory …

Recent researches on information theory shed new light on the continuous attempts to open
the black box of neural signal encoding. Inspired by the problem of lossy signal compression …

The implementation of deep neural networks requires a significant amount of memory and
computing power. As a result, they are often implemented in data centers or high-end …

Rotation Region Proposal Networks (RRPN) are used to generate rotated proposals with the
information of text angle for arbitrary oriented scene text detection (STD). However, the …

Hands is an important medium for human-computer interaction, and it is important for
computers to detect human hands in real time. However, the detection algorithm based on …

This work studies lag quasi-synchronization (LQS) for discrete-time master–slave (MS)
periodic neural networks (NNs) with the communication channel (CC) constraint. A …

To apply deep CNNs to mobile terminals and portable devices, many scholars have recently
worked on the compressing and accelerating deep convolutional neural networks. Based on …