LAUSR

Hopfield neural network, as a recurrent neural network, has been widely used to solve non-deterministic polynomial time-hard problems. However, the network tends to get trapped into local minima and thus converge to sub-optimal solutions. In this work, the intrinsic read noise in the memristive Hopfield network was harnessed as the random perturbation source to mitigate this problem. Firstly, the read noise in devices (TiN/TaO_x/HfO_x/TiN) at different resistance levels from a 1 Kb array was statistically measured, and the distribution of it was extracted. Then the effect of reading noise levels on the performance of a $64 \times 64$ network solver is quantitatively evaluated through confining all devices with the identical distribution. Based on such a strategy, the success probability, the distribution of distance, the energy consumption, and time to solution, at different noise levels and iteration cycles were investigated. The simulated results demonstrate that the intrinsic read noise in the resistive weight matrix is indeed helpful for the network to escape from local minima, serving as a useful computing resource.