Real-time processing of analog signals on accelerated neuromorphic hardware

2026-02-04 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Researchers have developed an alternative method for sensory processing on neuromorphic systems, directly injecting analog signals into the accelerated BrainScaleS-2 mixed-signal neuromorphic research platform. This approach bypasses power-intensive analog-to-digital and digital-to-analog conversions, making it suitable for efficient near-sensor processing. The BrainScaleS-2 ASIC, with its 1000-fold acceleration, was interfaced directly with microphones and a servo-motor-driven actuator. A spiking neural network was used to convert interaural time differences into a spatial code for sound source localization. This work demonstrates the first direct, continuous-valued sensor data injection into BrainScaleS-2's analog compute units and actuator control via its embedded microprocessors, enabling a fully on-chip processing pipeline from sensory input to physical action. The system successfully localized and aligned a servo motor with transient noise peaks in real-time.

Key takeaway

For research scientists developing real-time, low-power sensory processing systems, this direct analog injection method on neuromorphic hardware offers a significant path to efficiency. You should explore integrating analog sensors directly with platforms like BrainScaleS-2 to eliminate conversion overheads and achieve fully on-chip processing pipelines, potentially reducing latency and power consumption in edge AI applications.

Key insights

Direct analog signal injection into neuromorphic hardware enables efficient, real-time, near-sensor processing.

Principles

• Eliminate AD/DA conversions for efficiency.
• Utilize neuromorphic acceleration for real-time tasks.

Method

Directly inject analog sensor data into BrainScaleS-2's analog compute units, process with a spiking neural network, and control actuators via embedded microprocessors for a fully on-chip pipeline.

In practice

• Integrate analog sensors directly with neuromorphic chips.
• Control physical actuators from neuromorphic output.

Topics

• Neuromorphic Hardware
• Analog Signal Processing
• BrainScaleS-2
• Spiking Neural Networks
• Real-time Sensory Processing

Best for: Research Scientist, AI Researcher, AI Scientist, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.