Reduction and IR-drop compensations techniques for reliable neuromorphic computing systems

Abstract

Neuromorphic computing system (NCS) is a promising architecture to combat the well-known memory bottleneck in Von Neumann architecture. The recent breakthrough on memristor devices made an important step toward realizing a low-power, small-footprint NCS on-a-chip. However, the currently low manufacturing reliability of nano-devices and the voltage IR-drop along metal wires and memristors arrays severely limits the scale of memristor crossbar based NCS and hinders the design scalability. In this work, we propose a novel system reduction scheme that significantly lowers the required dimension of the memristor crossbars in NCS while maintaining high computing accuracy. An IR-drop compensation technique is also proposed to overcome the adverse impacts of the wire resistance and the sneak-path problem in large memristor crossbar designs. Our simulation results show that the proposed techniques can improve computing accuracy by 27.0% and 38.7% less circuit area compared to the original NCS design.