arXiv:2606.24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it difficult to strike a balance between accuracy and power consumption, thereby limiting their application on resource-constrained platforms. Neuromorphic architectures that perform spike-driven inference with modest energy budgets have recently been explored for vision and timeseries tasks. Motivated by these works, we propose EMRFormer, a novel end-to-end spiking nerural network (SNN) architectur
The increasing computational demands of advanced AI tasks and the proliferation of resource-constrained edge devices are driving the need for more energy-efficient AI architectures like neuromorphic computing.
This breakthrough offers a path to deploying sophisticated AI capabilities, such as automatic modulation recognition, on platforms where power consumption and computational resources are severely limited, unlocking new applications in embedded systems and defense.
Neuromorphic computing, specifically spiking neural networks, is demonstrated as a viable and power-efficient alternative to traditional deep learning for critical AI functions, challenging the current compute paradigm for certain applications.
- · Neuromorphic computing developers
- · Edge AI providers
- · Defense contractors
- · IoT device manufacturers
- · Traditional high-power AI accelerators
- · Developers solely focused on GPU-centric AI
Increased adoption of neuromorphic hardware in military, aerospace, and remote sensing applications for real-time signal processing.
A shift in compute supply chains towards specialized neuromorphic chip production and integration for low-power AI.
Enhanced battlefield awareness and autonomous capabilities for armed forces due to high-performance, low-power AI at the tactical edge, influencing future defence strategies.
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Read at arXiv cs.AI