Effective thermal management has become more essential as Neural Network (NN) accelerators continue to grow in power and complexity. High operating temperatures can degrade performance, accelerate hardware aging, increase power consumption, and raise failure rates. Simultaneously, these accelerators face up to 60% underutilization due to mismatches between network layers and the accelerator architecture. This work presents a technique that leverages idle resources to alleviate thermal hotspots in NN accelerators through strategic workload redistribution, all while preserving performance. Experimental results demonstrate that the proposed technique reduces the time that the NN accelerator spends in hotspot temperature by 47.2% compared to the state-of-the-art, leading to an improvement in the Mean Time to Failure (MTTF) by approximately 20.2%, which extends the lifespan of the NN accelerator by 25.1%. An improvement in MTTF allows for a reduction in the guardband for operating voltage and frequency, potentially leading to better performance and increased power efficiency.