In recent years, neural network accelerators have been shown to achieve both high energy
efficiency and high performance for a broad application scope within the important category …

Good old online backpropagation for plain multilayer perceptrons yields a very low 0.35%
error rate on the MNIST handwritten digits benchmark. All we need to achieve this best result …

Convolutional Neural Networks (CNN) are very computation-intensive. Recently, a lot of
CNN accelerators based on the CNN intrinsic parallelism are proposed. However, we …

Motivation The identification of bitter peptides through experimental approaches is an
expensive and time-consuming endeavor. Due to the huge number of newly available …

We present a novel mechanism to accelerate state-of-art Convolutional Neural Networks
(CNNs) on CPU-FPGA platform with coherent shared memory. First, we exploit Fast Fourier …

Abstract Machine-learning techniques have recently been proved to be successful in various
domains, especially in emerging commercial applications. As a set of machine-learning …

Although ReRAM-based convolutional neural network (CNN) accelerators have been widely
studied, state-of-the-art solutions suffer from either incapability of training (eg, ISSAC [1]) or …

Deep convolutional neural networks (DCNNs) have become a very powerful tool in visual
perception. DCNNs have applications in autonomous robots, security systems, mobile …

Convolutional networks (ConvNets) have become the dominant approach to semantic
image segmentation. Producing accurate, pixel--level labels required for this task is a …

Techniques in advanced deep learning provide improvements in one or more of accuracy,
performance, and energy efficiency, such as accuracy of learning, accuracy of prediction …