MIMICKING THE HUMAN BRAIN: NEUROMORPHIC ARCHITECTURE SOLUTIONS FOR AI ENERGY EFFICIENCY
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Energy Efficiency, Green AI, Memristors, Neuromorphic Computing, Spiking Neural Networks
Abstract
The exponential proliferation of Artificial Intelligence (AI) is currently constrained by the “memory wall” and excessive power consumption inherent in traditional Von Neumann architectures. This study addresses these physical limitations by proposing a bio-inspired neuromorphic architecture that integrates memristive crossbar arrays with event-driven Spiking Neural Networks (SNNs) to mimic biological synaptic efficiency. The research employs a quantitative cross-layer simulation framework to benchmark the proposed design against industry-standard GPUs and TPUs, utilizing standard datasets to evaluate inference latency, power dissipation, and classification accuracy. Results indicate that the neuromorphic architecture achieves a reduction in energy consumption by orders of magnitude (0.12 pJ/operation) compared to baseline accelerators, with power usage scaling linearly with input sparsity. Although a minor trade-off in precision was observed due to device stochasticity, the system maintained a competitive classification accuracy of 92.4%. The study concludes that mimicking the asynchronous nature of the human brain offers a sustainable paradigm for “Green AI,” validating neuromorphic computing as a critical solution for overcoming the energy crisis in next-generation edge intelligence and autonomous systems.
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