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New publication: "Real-Time Processing of Analog Signals on Accelerated Neuromorphic Hardware"
We are happy to announce that our publication "Real-Time Processing of Analog Signals on Accelerated Neuromorphic Hardware" is now available as part of the NICE 2026 proceedings – make sure to check out the manuscript (10.1109/NICE69539.2026.11567519)!
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—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.
