High Speed Binary Neural Network Hardware Accelerator Relied on Optical NEMS

Abstract

In this article, an electrostatically-actuated NEMS XOR gate is proposed based on photonic crystals for hardware implementation of binary neural networks. The device includes a 2D photonic crystal which is set on a movable electrode to implement the XOR logic using the transmission of specific wavelengths to the output. This design represents the importance of the proposed structure in which the logic gate operation is not dependent on the contact of its conductive layers. Consequently, one of the major issues in MEMS-based logic gates, which is due to the contact of the operating electrodes and may cause stiction problem, reducing the reliability of the system, can be tackled by the present approach. Furthermore, according to the simulation results, the functional characteristics of the present NEMS XOR gate are obtained as follows: pull-in voltage of V _p = 8V, operating voltage of V _o = 10V and switching time of t _s = 4 μs. The results also show that the proposed design provides a classification error rate of between 1% to 12%, while used in neural network implementation. This error can be negligible compared to the state-of-the-art designs in neural network implementation. These appropriate parameters of the present NEMS gate make it a promising choice for the implementation of neural networks with a high network accuracy even in the presence of significant process variations.