Deep Neural Networks (DNNs) have shown significant advantages in many domains, such
as pattern recognition, prediction, and control optimization. The edge computing demand in …

Deep Neural Networks (DNN) have shown significant advantages in many domains such as
pattern recognition, prediction, and control optimization. The edge computing demand in the …

Large-scale neural network accelerators are often implemented as a many-core chip and
rely on a network-on-chip to manage the huge amount of inter-neuron traffic. The baseline …

Multi-layer perceptron (MLP) is a class of Artificial Neural Networks widely used in
regression, classification, and prediction. To accelerate the training of MLP, more cores can …

Abstract Convolutional Neural Networks (CNNs) have a wide range of applications due to
their superior performance in image and pattern classification. However, the performance of …

In the last few decades, the concept of Wireless Network-on-chip (WiNoC) has emerged as a
promising alternative for Multiprocessor Systems on Chip (MPSOC) to achieve reliable and …

Emerging neural network accelerators are often implemented as many-core chips and rely
on a network-on-chip to handle the huge amount of inter-neuron traffic. The wellknown mesh …

A hardware friendly topology of neural network is proposed in this work. Instead of full
connections between neighbor layers, the bisection-propagation from “parents” to “twins” is …

This paper describes a novel video specific instruction set architecture for ASIP design. With
SIMD (Single Instruction Multiple Data) instructions, and video specific instructions, an …