John P. Hayes
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View article: Multiplexer-Majority Chains: Managing Correlation and Cost in Stochastic Number Generation
Multiplexer-Majority Chains: Managing Correlation and Cost in Stochastic Number Generation Open
High-cost stochastic number generators (SNGs) are the main source of stochastic numbers (SNs) in stochastic computing. Interacting SNs must usually be uncorrelated for satisfactory results, but deliberate correlation can sometimes dramatic…
View article: Innate immune cGAS/STING in HFD‐induced obesity, prediabetes, and cognitive impairment
Innate immune cGAS/STING in HFD‐induced obesity, prediabetes, and cognitive impairment Open
Background Increasing in parallel with the worldwide aging population are rates of cognitive impairment and dementia, including Alzheimer’s Disease and Alzheimer’s Disease Related‐Dementia. Understanding underlying mechanisms that promote …
View article: CeMux: Maximizing the Accuracy of Stochastic Mux Adders and an Application to Filter Design
CeMux: Maximizing the Accuracy of Stochastic Mux Adders and an Application to Filter Design Open
Stochastic computing (SC) is a low-cost computational paradigm that has promising applications in digital filter design, image processing, and neural networks. Fundamental to these applications is the weighted addition operation, which is …
View article: Guest Editors’ Introduction: Stochastic Computing for Neuromorphic Applications
Guest Editors’ Introduction: Stochastic Computing for Neuromorphic Applications Open
Editor's notes: This extended editorial provides an introduction into stochastic computing (SC) and its usage in emerging neuromorphic applications. It covers SC primitives and the tradeoffs that occur when designing larger SC-based system…
View article: CeMux: Maximizing the Accuracy of Stochastic Mux Adders and an\n Application to Filter Design
CeMux: Maximizing the Accuracy of Stochastic Mux Adders and an\n Application to Filter Design Open
Stochastic computing (SC) is a low-cost computational paradigm that has\npromising applications in digital filter design, image processing and neural\nnetworks. Fundamental to these applications is the weighted addition operation\nwhich is…
View article: Removing constant‐induced errors in stochastic circuits
Removing constant‐induced errors in stochastic circuits Open
Stochastic computing (SC) computes with probabilities using randomised bit‐streams and standard logic circuits. Its major advantages include ultra‐low area and power, coupled with high error tolerance. However, due to its randomness featur…
View article: On the Role of Sequential Circuits in Stochastic Computing
On the Role of Sequential Circuits in Stochastic Computing Open
Interest in stochastic computing (SC) has been growing due to the need for low-cost circuitry in areas like image processing and machine learning. While combinational stochastic circuits are relatively easy to design, their limitations suc…
View article: Energy-efficient hybrid stochastic-binary neural networks for near-sensor computing
Energy-efficient hybrid stochastic-binary neural networks for near-sensor computing Open
Recent advances in neural networks (NNs) exhibit unprecedented success at transforming large, unstructured data streams into compact higher-level semantic information for tasks such as handwriting recognition, image classification, and spe…