Stochastic Computing (SC) has the potential to dramatically improve important nanoscale
circuit metrics, including area and power dissipation, for implementing complex digital …

General matrix multiplication (GEMM) is universal in various applications, such as signal
processing, machine learning, and computer vision. Conventional GEMM hardware …

As a special type of recurrent neural networks (RNN), Long Short Term Memory (LSTM) is
capable of processing sequential data with a great improvement in accuracy and is widely …

General matrix multiply (GEMM) is an important operation in broad applications, especially
the thriving deep neural networks. To achieve low power consumption for GEMM …

The traditional neural network Intelligent chip has the problem of high power consumption
due to classical computing architecture, limiting the development of neural network …

This article presents novel designs for stochastic computing (SC)-based dividers, which
promise low latency, high energy efficiency, as well as high accuracy for error-tolerant …

Stochastic computing (SC) is a re-emerging computing paradigm providing low-cost and
noise-tolerant designs for a wide range of arithmetic operations. SC circuits operate on …

General matrix multiplication (GEMM) is pervasive in various domains, such as signal
processing, computer vision, and machine learning. Conventional binary architectures for …

Stochastic computing is a statistical computing scheme that represents data as serial bit
streams to greatly reduce hardware complexity. The key trade-off is that processing more …

Binarization plays a key role in image processing. Its performance directly affects the
success of subsequent character segmentation and recognition. The Phansalkar algorithm …