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 …

Effectively tackling the upcoming “zettabytes” data explosion requires a huge quantum leap
in our computing power and energy efficiency. However, with the Moore's law dwindling …

Stochastic computing (SC), which is an approximate computation with probabilities, has
attracted attention as an alternative to deterministic computing. In this paper, we discuss a …

Stochastic computing (SC) often requires long stochastic sequences and, thus, a long
latency to achieve accurate computation. The long latency leads to an inferior performance …

Stochastic computing, which is an approximate computation with probabilities (called
stochastic numbers), draws attention as an alternative method of deterministic computing. In …

The continued scaling of feature sizes in integrated circuit technology leads to more
uncertainty and unreliability in circuit behavior. Maintaining the paradigm of deterministic …

This paper addresses implementation of digital IIR filters using stochastic computing.
Stochastic computing requires fewer logic gates and is inherently fault-tolerant. Thus, these …

Stochastic computing (SC) is an alternative computing technique for embedded systems
which offers lower area and power, and better error resilience compared to binary-encoded …

Strassen's algorithm for matrix multiplication gains its lower arithmetic complexity at the
expense of reduced locality of reference, which makes it challenging to implement the …

Gradient descent (GD) is a widely used optimization algorithm in machine learning. In this
paper, a novel stochastic computing GD circuit (SC-GDC) is proposed by encoding the …