End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing

2026-06-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & & Innovation · Depth: Expert, quick

Summary

EMRFormer is a novel end-to-end spiking neural network (SNN) architecture designed for automatic modulation recognition (AMR) on resource-constrained platforms. Addressing the high computational cost of traditional deep learning methods, EMRFormer applies a spike-driven transformer to neuromorphic hardware. The model integrates an adaptive spike encoder and Integer Leaky Integrate-and-Fire neurons to enhance representational capacity and mitigate information degradation. By combining spike-separable Convolution Neural Networks (SSCNN) with Spike-Driven Transformers (SpikeFormer), EMRFormer effectively extracts multi-scale temporal features from raw IQ waveforms. Experimental results demonstrate that EMRFormer achieves high accuracy across various mainstream datasets, outperforming baselines and maintaining strong performance in low signal-to-noise environments. Furthermore, it reduces theoretical energy consumption by over 90% and, when evaluated on a KA200 neuromorphic chip, shows up to 5 times power reduction compared to a 3090 GPU or Orin NX.

Key takeaway

For Machine Learning Engineers deploying automatic modulation recognition (AMR) models on resource-constrained edge devices, EMRFormer offers a compelling solution. You should consider integrating spiking neural networks and spike-driven transformers to achieve significant power reductions, up to 5 times compared to GPUs, while maintaining high accuracy. This approach enables robust AMR performance even in low signal-to-noise environments, making it ideal for power-sensitive applications.

Key insights

EMRFormer leverages spiking neural networks and spike-driven transformers for energy-efficient automatic modulation recognition.

Principles

• Neuromorphic architectures balance accuracy and power for constrained platforms.
• Adaptive spike encoding enhances SNN representational capacity.

Method

EMRFormer integrates an adaptive spike encoder, Integer Leaky Integrate-and-Fire neurons, and combines SSCNN with SpikeFormer to process raw IQ waveforms for AMR.

In practice

• Deploy AMR models on neuromorphic chips for power reduction.
• Apply spike-driven transformers for efficient time-series feature extraction.

Topics

• Neuromorphic Computing
• Spiking Neural Networks
• Automatic Modulation Recognition
• Spike-Driven Transformers
• Energy Efficiency
• Edge AI

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.