Real-time processing of analog signals on accelerated neuromorphic hardware

• URL: http://arxiv.org/abs/2602.04582v1
• Date: Wed, 04 Feb 2026 14:07:54 GMT
• Title: Real-time processing of analog signals on accelerated neuromorphic hardware
• Authors: Yannik Stradmann, Johannes Schemmel, Mihai A. Petrovici, Laura Kriener,
• Abstract summary: We present a direct analog signal injection system for processing sensory data.<n>We employ a spiking neural network to transform data into aural code and predict the location of sound sources.<n>We demonstrate this by programming a servo-motor actuator to localize near noise peaks in realtime.
• Score: 1.4274628617282759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sensory processing with neuromorphic systems is typically done by using either event-based sensors or translating input signals to spikes before presenting them to the neuromorphic processor. Here, we offer an alternative approach: direct analog signal injection eliminates superfluous and power-intensive analog-to-digital and digital-to-analog conversions, making it particularly suitable for efficient near-sensor processing. We demonstrate this by using the accelerated BrainScaleS-2 mixed-signal neuromorphic research platform and interfacing it directly to microphones and a servo-motor-driven actuator. Utilizing BrainScaleS-2's 1000-fold acceleration factor, we employ a spiking neural network to transform interaural time differences into a spatial code and thereby predict the location of sound sources. Our primary contributions are the first demonstrations of direct, continuous-valued sensor data injection into the analog compute units of the BrainScaleS-2 ASIC, and actuator control using its embedded microprocessors. This enables a fully on-chip processing pipeline$\unicode{x2014}$from sensory input handling, via spiking neural network processing to physical action. We showcase this by programming the system to localize and align a servo motor with the spatial direction of transient noise peaks in real-time.

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