Search Results for author:
Found 2 papers, 0 papers with code
Process, Bias and Temperature Scalable CMOS Analog Computing Circuits for Machine Learning
Analog computing is attractive compared to digital computing due to its potential for achieving higher computational density and higher energy efficiency.
Bias-Scalable Near-Memory CMOS Analog Processor for Machine Learning
In this paper, we demonstrate the implementation of bias-scalable approximate analog computing circuits using the generalization of the margin-propagation principle called shape-based analog computing (S-AC).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
