Printed Stochastic Computing Neural Networks

Dennis D. Weller, Nathaniel Bleier, Michael Hefenbrock, Jasmin Aghassi-Hagmann, Michael Beigl, Rakesh Kumar, Mehdi B. Tahoori

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Printed electronics (PE) offers flexible, extremely low-cost, and on-demand hardware due to its additive manufacturing process, enabling emerging ultra-low-cost applications, including machine learning applications. However, large feature sizes in PE limit the complexity of a machine learning classifier (e.g., a neural network (NN)) in PE. Stochastic computing Neural Networks (SC-NNs) can reduce area in silicon technologies, but still require complex designs due to unique implementation tradeoffs in PE. In this paper, we propose a printed mixed-signal system, which substitutes complex and power-hungry conventional stochastic computing (SC) components by printed analog designs. The printed mixed-signal SC consumes only 35% of power consumption and requires only 25% of area compared to a conventional 4-bit NN implementation. We also show that the proposed mixed-signal SC-NN provides good accuracy for popular neural network classification problems. We consider this work as an important step towards the realization of printed SC-NN hardware for near-sensor-processing.

Original languageEnglish (US)
Title of host publicationProceedings of the 2021 Design, Automation and Test in Europe, DATE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9783981926354
StatePublished - Feb 1 2021
Externally publishedYes
Event2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 - Virtual, Online
Duration: Feb 1 2021Feb 5 2021

Publication series

NameProceedings -Design, Automation and Test in Europe, DATE
ISSN (Print)1530-1591


Conference2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
CityVirtual, Online


  • electrolyte-gated transistors
  • neural networks
  • printed electronics
  • stochastic computing

ASJC Scopus subject areas

  • General Engineering


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