Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons

• URL: http://arxiv.org/abs/2411.08674v3
• Date: Mon, 09 Dec 2024 13:10:46 GMT
• Title: Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons
• Authors: Florentia Afentaki, Paula Carolina Lozano Duarte, Georgios Zervakis, Mehdi B. Tahoori,
• Abstract summary: Printed electronics technology offers a cost-and fully-customizable solution to computational needs beyond traditional silicon technologies. Low-resolution fabrication of printedelectronics poses a challenge for integrating complex designs like those of machine learn-ing (ML) classification systems.
• Score: 0.9406794506458744
• License:
• Abstract: Printed electronics technology offers a cost-effectiveand fully-customizable solution to computational needs beyondthe capabilities of traditional silicon technologies, offering ad-vantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printedelectronics, which results in large feature sizes, poses a challengefor integrating complex designs like those of machine learn-ing (ML) classification systems. Current literature optimizes onlythe Multilayer Perceptron (MLP) circuit within the classificationsystem, while the cost of analog-to-digital converters (ADCs)is overlooked. Printed applications frequently require on-sensorprocessing, yet while the digital classifier has been extensivelyoptimized, the analog-to-digital interfacing, specifically the ADCs,dominates the total area and energy consumption. In this work,we target digital printed MLP classifiers and we propose thedesign of customized ADCs per MLP's input which involvesminimizing the distinct represented numbers for each input,simplifying thus the ADC's circuitry. Incorporating this ADCoptimization in the MLP training, enables eliminating ADC levelsand the respective comparators, while still maintaining highclassification accuracy. Our approach achieves 11.2x lower ADCarea for less than 5% accuracy drop across varying MLPs.

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