Thermal-Aware Layout Optimization and Mapping Methods for Resistive Neuromorphic Engines

Resistive neuromorphic engines can accelerate spiking neural network tasks with memristor crossbars. However, the stored weight is influenced by the temperature, which leads to accuracy and endurance degradation. The higher the temperature is, the larger the influence is. In this work, we propose a cross-array mapping method and a layout optimization method to reduce the thermal effect with the consideration of input distribution, weight value and layout of memristor crossbars. Experimental results show that our method reduces the peak temperature up to 10.4K and improves the endurance up to 1.72×.