Abstract Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in
many recognition problems. However, CNN models are computation-intensive and require …

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

Stochastic computing (SC) is an alternative computing paradigm that processes data in the
form of uniform bit-streams. SC is fault-tolerant and can compute on small, efficient circuits …

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 …

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

Stochastic computing (SC) has emerged as an alternative to conventional computing with
weighted binary representation. The operations in SC can be performed through simple …

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

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

Major sources of error in stochastic circuits are correlation errors, soft errors, and random
fluctuation errors, which impact the circuit's accuracy and reliability. The soft error has the …