A 32-channel event-based bio-signal analog front-end with adaptive delta and pulse frequency encoding

2026-07-14

AI Summary

This work addresses the high power consumption and substantial data redundancy of conventional biosignal acquisition systems, which hinder their suitability for neuromorphic computing and brain–machine interfaces requiring low-power, efficient online processing. The paper presents a 32-channel event-driven analog front-end ASIC fabricated in 180 nm CMOS technology, integrating a programmable multi-channel front end with a dual-mode encoder supporting pulse frequency modulation (PFM) and adaptive asynchronous Delta modulation (aADM). The aADM scheme features real-time adaptive scaling, dynamically adjusting the data rate according to the signal envelope to significantly enhance compression efficiency while preserving signal fidelity. The chip is compatible with spiking neural network (SNN) processor interfaces, providing a high-energy-efficiency, high-compression-ratio hardware foundation for end-to-end neuromorphic systems and online brain–machine interfaces.

Abstract

Low-power event-based Analog Front-Ends (AFEs) are essential for building efficient, end-to-end neuromorphic signal processing systems. In this paper, we present an event-based AFE Application-Specific Integrated Circuit (ASIC) optimized for biomedical signal acquisition and encoding. The chip features 32 independently programmable input channels with dual-mode encoding mechanism outputs, comprising Pulse Frequency Modulation (PFM) and adaptive Asynchronous Delta Modulator (aADM) circuits. The aADM encoder provides an auto-scaling mechanism that adapts the encoding data-rate based on the input signal envelope in real-time, enabling very high data compression for low-power information transmission. This approach paves the way toward adaptive wireless communication of neural signals for on-line processing in brain-computer interfaces. Fabricated in a 180 nm CMOS process, the proposed ASIC offers a highly configurable interface compatible with state-of-the-art Spiking Neural Network (SNN) neuromorphic processors.

Problem

• event-based
• analog front-end
• bio-signal
• adaptive encoding
• brain-computer interface

Innovation

• event-based AFE
• adaptive Asynchronous Delta Modulator
• Pulse Frequency Modulation
• neuromorphic processing
• data compression