Neuromorphic Computing Architectures for Energy-Efficient AI Applications

Abstract

This paper presents novel neuromorphic computing architectures designed to achieve high computational efficiency for artificial intelligence applications. We developed a spiking neural network processor that consumes 100x less energy than conventional GPU-based systems while maintaining comparable performance. The architecture mimics biological neural systems to enable event-driven computation. Benchmark results on image classification and natural language processing tasks demonstrate the potential for deploying AI in edge devices with limited power budgets.