Process, Bias, and Temperature Scalable CMOS Analog Computing Circuits for Machine Learning

Pratik Kumar, Ankita Nandi, Shantanu Chakrabartty, Chetan Singh Thakur

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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 shape-based analog computing (S-AC) circuits that can be easily cross-mapped across different process nodes. Similar to digital designs S-AC designs can also be scaled for precision, speed, and power. As a proof-of-concept, we show several examples of S-AC circuits implementing mathematical functions that are commonly used in machine learning architectures. Using circuit simulations we demonstrate that the circuit input/output characteristics remain robust when mapped from a planar CMOS 180nm process to a FinFET 7nm process. Also, using benchmark datasets we demonstrate that the classification accuracy of a S-AC based neural network remains robust when mapped across the two processes and to changes in temperature.

Original languageEnglish
Pages (from-to)128-141
Number of pages14
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number1
StatePublished - Jan 1 2023


  • Machine learning
  • approximate computing
  • margin propagation
  • process scalability
  • shape-based computing


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