A survey of stochastic computing neural networks for machine learning applications
Neural networks (NNs) are effective machine learning models that require significant
hardware and energy consumption in their computing process. To implement NNs …
hardware and energy consumption in their computing process. To implement NNs …
Survey of stochastic computing
Stochastic computing (SC) was proposed in the 1960s as a low-cost alternative to
conventional binary computing. It is unique in that it represents and processes information in …
conventional binary computing. It is unique in that it represents and processes information in …
The promise and challenge of stochastic computing
Stochastic computing (SC) is an unconventional method of computation that treats data as
probabilities. Typically, each bit of an N-bit stochastic number (SN) **s randomly chosen to …
probabilities. Typically, each bit of an N-bit stochastic number (SN) **s randomly chosen to …
Stochastic neural computation. I. Computational elements
BD Brown, HC Card - IEEE Transactions on computers, 2001 - ieeexplore.ieee.org
This paper examines a number of stochastic computational elements employed in artificial
neural networks, several of which are introduced for the first time, together with an analysis …
neural networks, several of which are introduced for the first time, together with an analysis …
VLSI implementation of deep neural network using integral stochastic computing
The hardware implementation of deep neural networks (DNNs) has recently received
tremendous attention: many applications in fact require high-speed operations that suit a …
tremendous attention: many applications in fact require high-speed operations that suit a …
Scope: A stochastic computing engine for dram-based in-situ accelerator
Memory-centric architecture, which bridges the gap between compute and memory, is
considered as a promising solution to tackle the memory wall and the power wall. Such …
considered as a promising solution to tackle the memory wall and the power wall. Such …
Exploiting errors for efficiency: A survey from circuits to applications
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 …
tolerates the effects of noise in its execution, hardware, system software, and programming …
A native stochastic computing architecture enabled by memristors
A two-terminal memristor device is a promising digital memory for its high integration
density, substantially lower energy consumption compared to CMOS, and scalability below …
density, substantially lower energy consumption compared to CMOS, and scalability below …
FPGA-based implementation of deep neural network using stochastic computing
M Nobari, H Jahanirad - Applied Soft Computing, 2023 - Elsevier
A serious challenge in artificial real-time applications is the hardware implementation of
deep neural networks (DNN). Among various methods, stochastic computing (SC)-based …
deep neural networks (DNN). Among various methods, stochastic computing (SC)-based …
Using stochastic computing to reduce the hardware requirements for a restricted Boltzmann machine classifier
Artificial neural networks are powerful computational systems with interconnected neurons.
Generally, these networks have a very large number of computation nodes which forces the …
Generally, these networks have a very large number of computation nodes which forces the …