Design and Optimization of Neuromorphic Hardware Accelerators for Energy-efficient Edge Ai Inference a Cross-layer Co-design Approach Integrating Spiking Neural Networks, Memristive Architectures, and Hardware-aware Model Compression
Year: 2025
Type: article
Abstract: As Edge AI continues to grow, energy-efficient inference becomes increasingly essential for real-time applications in constrained environments. This paper presents a cross-layer co-design framework integrating Spiking Neural Networks (SNNs), memristive-based neuromorphic hardware, and hardware-aware model compression techniques to optimize edge AI performance. By unifying algorithm-hardware co-optimization, we achieve significant reductions in latency and power consumption without compromising accuracy. Experimental evaluations on representative workloads demonstrate that our framework provides up to 85% energy savings compared to traditional architectures, while maintaining competitive inference accuracy.
Author: Researcher
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6931229437
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