1,867 research outputs found
A Review of Bayesian Methods in Electronic Design Automation
The utilization of Bayesian methods has been widely acknowledged as a viable
solution for tackling various challenges in electronic integrated circuit (IC)
design under stochastic process variation, including circuit performance
modeling, yield/failure rate estimation, and circuit optimization. As the
post-Moore era brings about new technologies (such as silicon photonics and
quantum circuits), many of the associated issues there are similar to those
encountered in electronic IC design and can be addressed using Bayesian
methods. Motivated by this observation, we present a comprehensive review of
Bayesian methods in electronic design automation (EDA). By doing so, we hope to
equip researchers and designers with the ability to apply Bayesian methods in
solving stochastic problems in electronic circuits and beyond.Comment: 24 pages, a draft version. We welcome comments and feedback, which
can be sent to [email protected]
Analog Gated Recurrent Neural Network for Detecting Chewing Events
We present a novel gated recurrent neural network to detect when a person is
chewing on food. We implemented the neural network as a custom analog
integrated circuit in a 0.18 um CMOS technology. The neural network was trained
on 6.4 hours of data collected from a contact microphone that was mounted on
volunteers' mastoid bones. When tested on 1.6 hours of previously-unseen data,
the neural network identified chewing events at a 24-second time resolution. It
achieved a recall of 91% and an F1-score of 94% while consuming 1.1 uW of
power. A system for detecting whole eating episodes -- like meals and snacks --
that is based on the novel analog neural network consumes an estimated 18.8uW
of power.Comment: 11 pages, 16 figure
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