In recent decades, artificial intelligence has been successively employed in the fields of
finance, commerce, and other industries. However, imitating high-level brain functions, such …

A serious challenge in artificial real-time applications is the hardware implementation of
deep neural networks (DNN). Among various methods, stochastic computing (SC)-based …

Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic
computing whereby a single logic gate can perform the arithmetic operation by exploiting the …

Stochastic computing (SC) has a substantial amount of study on application-specific
integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the …

Stochastic computing requires random number generators to generate stochastic
sequences that represent probability values. In the case of an 8-bit operation, a 256-bit …

Stochastic computing (SC) is an emerging paradigm that offers hardware-efficient solutions
for developing low-cost and noise-robust architectures. In SC, deterministic logic systems …

Random pulse computing (RPC), the third paradigm along with digital and quantum
computing, draws inspiration from biology, particularly the functioning of neurons. Here, we …

Stochastic mean circuits (SMCs) are at the core of mean filtering and neural networks, but
they have not been fully investigated in stochastic computing (SC). In this brief, SMCs in the …

Stochastic computing (SC) is a probabilistic-based processing methodology that has
emerged as an energy-efficient solution for implementing image processing and deep …

Stochastic computing is an emerging scientific field pushed by the need for developing high-
performance artificial intelligence systems in hardware to quickly solve complex data …