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Editorial: Algorithm-hardware co-optimization in neuromorphic computing for efficient AI
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DOI：10.3389/fnins.2025.1746610.png)
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Abstract
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Neuromorphic computing holds the promise of sustainable AI by combining brain-inspired models 4 with event-driven; massively parallel hardware. However; a central question remains: when and how 5 do neuromorphic systems convert their architectural advantages into effective end-to-end efficiency 6 for real-world tasks? This Research Topic presents six contributions that address this question from 7 various perspectives. Specifically; it explores training methods that minimize timesteps and memory 8 usage; hardware-aware algorithms and quantization; emulation techniques that mitigate risks associated 9 with analog platforms; and mapping and scheduling strategies that enhance utilization on many-core 10 neuromorphic chips. 11We are pleased to present a series of innovative research articles in this field that introduce the following
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