Energy-Efficient Neuromorphic Computing with Spiking Neural Networks on Edge Devices: A Dynamic Programming Approach

The proliferation of Internet of Things devices has necessitated a paradigm shift from centralized cloud computing to edge computing, primarily to mitigate latency and bandwidth constraints. However, deploying complex deep learning models on resource-constrained edge devices remains a significant challenge due to power limitations. Neuromorphic computing, specifically utilizing Spiking Neural Networks, offers a promising solution by mimicking the event-driven, sparse processing capabilities of the biological brain. Despite their theoretical energy efficiency, the optimal mapping of Spiking Neural Networks onto hardware architectures with limited cores and memory is a non-trivial problem, often resulting in suboptimal energy utilization when heuristic methods are employed. This paper proposes a novel framework that leverages Dynamic Programming to optimize the placement and scheduling of spiking neurons on multicoreneuromorphic processors. By formulating the mapping problem as a multi-stage decision process, our approach systematically balances the trade-offs between inter-core communication costs and computational load balancing. We demonstrate that the proposed method significantly reduces energy consumption compared to standard greedy and random mapping strategies while maintaining high inference accuracy. The results underscore the viability of Dynamic Programming as a deterministic tool for enhancing the operational efficiency of next-generation edge intelligence.