When a computational task tolerates a relaxation of its specification or when an algorithm
tolerates the effects of noise in its execution, hardware, system software, and programming …

Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the
brain's structure to offer a powerful and efficient processing and learning model. In HDC, the …

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

Images are often corrupted with noise. As a result, noise reduction is an important task in
image processing. Common noise reduction techniques, such as mean or median filtering …

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 rapid computerized simulation of stochastic computing (SC) systems is a challenging
problem. A method for agile simulation of SC image processing is proposed in this work. The …

Stochastic Computing (SC) is an alternative computing paradigm that promises high
robustness to noise and outstanding area-and power-efficiency compared to traditional …

Hyperdimensional vector processing is a nascent computing approach that mimics the brain
structure and offers lightweight, robust, and efficient hardware solutions for different learning …

This article addresses polynomial computation with unipolar stochastic logic by exploiting
correlation between the bit-streams. The AND-OR, double-NAND, OR-AND and double …

Low-discrepancy (LD) bit-streams have been proposed to improve the accuracy and
computation speed of stochastic computing (SC) circuits. These bit-streams are …