Convolutional neural networks (CNNs) have proven to be a disruptive technology in most
vision, speech and image processing tasks. Given their ubiquitous acceptance, the research …

Neural Networks (NN) are a family of models for a broad range of emerging machine
learning and pattern recondition applications. NN techniques are conventionally executed …

An ever-increasing number of computer vision and image/video processing challenges are
being approached using deep convolutional neural networks, obtaining state-of-the-art …

In this paper, we present an energy-efficient CNN processor with 4 key features:(1) a CNN-
optimized neuron processing engine (NPE),(2) a dual-range multiplyaccumulate (DRMAC) …

A booming number of computer vision, speech recognition, and signal processing
applications, are increasingly benefiting from the use of deep convolutional neural networks …

High-performance computing systems are moving towards 2.5 D and 3D memory
hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to …

Hardware design of deep convolutional neural networks (CNNs) faces challenges of high
computational complexity and data bandwidth as well as huge divergence in different CNN …

Executing a neural network includes generating an output tile of a first layer of the neural
network by processing an input tile to the first layer and storing the output tile of the first layer …

Heterogeneous embedded systems on chip (HESoCs) co-integrate a standard host
processor with programmable manycore accelerators (PMCAs) to combine general-purpose …

Hardware accelerators for convolution neural networks (CNNs) enable real-time
applications of artificial intelligence technology. However, most of the existing designs suffer …