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Low-power event-based analog front-ends (AFE) are a crucial componentrequired to build efficient end-to-end neuromorphic processing systems for edgecomputing. Although several neuromorphic chips have been developed forimplementing spiking neural networks (SNNs) and solving a wide range of sensoryprocessing tasks, there are only a few general-purpose analog front-end devicesthat can be used to convert analog sensory signals into spikes and interfacedto neuromorphic processors. In this work, we present a novel, highlyconfigurable analog front-end chip, denoted as SPAIC (signal-to-spike converterfor analog AI computation), that offers a general-purpose dual-mode analogsignal-to-spike encoding with delta modulation and pulse frequency modulation,with tunable frequency bands. The ASIC is designed in a 180 nm process. Itsupports and encodes a wide variety of signals spanning 4 orders of magnitudein frequency, and provides an event-based output that is compatible withexisting neuromorphic processors. We validated the ASIC for its functions andpresent initial silicon measurement results characterizing the basic buildingblocks of the chip.

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