Deep Learning, as an important branch of machine learning and neural network, is playing
an increasingly important role in a number of fields like computer vision, natural language …

This paper presents an efficient unipolar stochastic computing hardware for convolutional
neural networks (CNNs). It includes stochastic ReLU and optimized max function, which are …

Deep Convolutional Neural Networks (DCNN), a branch of Deep Neural Networks which
use the deep graph with multiple processing layers, enables the convolutional model to …

There has been a body of research to use stochastic computing (SC) for the implementation
of neural networks, in the hope that it will reduce the area cost and energy consumption …

In this paper, we propose reconfigurable, low-cost and readily available hardware
architecture for an artificial neuron. This is used to build a feed-forward artificial neural …

Neural networks are becoming prevalent in many areas, such as pattern recognition and
medical diagnosis. Stochastic computing is one potential solution for neural networks …

Stochastic Computing (SC) is an alternative design paradigm particularly useful for
applications where cost is critical. SC has been applied to neural networks, as neural …

In recent years, Deep Convolutional Neural Network (DCNN) has become the dominant
approach for almost all recognition and detection tasks and outperformed humans on certain …

-This paper proposes and studies the premise of three-dimensional (3D) reconfigurable
vector neural networks (3DV NNs). We research a neurocomputing paradigm to accomplish …

Computation performed on stochastic bit streams is less efficient than that based on a binary
radix because of its long latency. However, for certain complex arithmetic operations …