SPAIC: A sub-μW/Channel, 16-Channel General-Purpose Event-Based Analog Front-End with Dual-Mode Encoders

Shyam Narayanan, Matteo Cartiglia, Arianna Rubino, Charles Lego, Charlotte Frenkel, Giacomo Indiveri

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


Low-power event-based analog front-ends (AFE) are a crucial component required to build efficient end-to-end neuromorphic processing systems for edge computing. Although several neuromorphic chips have been developed for implementing spiking neural networks (SNNs) and solving a wide range of sensory processing tasks, there are only a few general-purpose analog front-end devices that can be used to convert analog sensory signals into spikes and interfaced to neuromorphic processors. In this work, we present a novel, highly configurable analog front-end chip, denoted as "SPAIC" (signal-to-spike converter for analog AI computation), that offers a general-purpose dual-mode analog signal-to-spike encoding with delta modulation and pulse frequency modulation, with tunable frequency bands. The ASIC is designed in a 180nm process. It supports and encodes a wide variety of signals spanning 4 orders of magnitude in frequency, and provides an event-based output that is compatible with existing neuromorphic processors. We validated the ASIC for its functions and present initial silicon measurement results characterizing the basic building blocks of the chip.
Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Place of PublicationDanvers
Number of pages5
ISBN (Electronic)979-8-3503-0026-0
ISBN (Print)979-8-3503-0027-7
Publication statusPublished - 2023
Event2023 IEEE Biomedical Circuits and Systems Conference (BioCAS) - Toronto, Canada
Duration: 19 Oct 202321 Oct 2023


Conference2023 IEEE Biomedical Circuits and Systems Conference (BioCAS)
City Toronto

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Neuromorphic
  • Analog Front-End (AFE)
  • Encoder
  • Spiking Neural Network (SNN)


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