Process, Bias and Temperature Scalable CMOS Analog Computing Circuits for Machine Learning. (arXiv:2205.05664v2 [cs.AR] UPDATED)

Analog computing is attractive compared to digital computing due to its
potential for achieving higher computational density and higher energy
efficiency. However, unlike digital circuits, conventional analog computing
circuits cannot be easily mapped across different process nodes due to
differences in transistor biasing regimes, temperature variations and limited
dynamic range. In this work, we generalize the previously reported
margin-propagation-based analog computing framework for designing novel
\textit{shape-based analog computing} (S-AC) circuits that can be easily
cross-mapped across different process nodes. Similar …

analog ar arxiv bias cmos computing learning machine machine learning process scalable