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Research PaperResearchia:202607.15040

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

Narayanan Shyam

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 a...

Submitted: July 15, 2026Subjects: Neuroscience; Bio-AI Interfaces

Description / Details

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.


Source: arXiv:2607.12901v1 - http://arxiv.org/abs/2607.12901v1 PDF: https://arxiv.org/pdf/2607.12901v1 Original Link: http://arxiv.org/abs/2607.12901v1

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Submission Info
Date:
Jul 15, 2026
Topic:
Bio-AI Interfaces
Area:
Neuroscience
Comments:
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A 32-channel event-based bio-signal analog front-end with adaptive delta and pulse frequency encoding | Researchia