Memristor-based Offset Cancellation Technique in Analog Crossbars.

Analog computing platforms have been a popular and promising research area that suggest efficient ways of computation compared to its digital counterparts. Memristor based crossbars drew attention by computing the vector-matrix calculation intensive tasks such as Artificial Intelligence (AI) and Machine Learning (ML) in one time step. Although they provide an energy efficient way of computing these tasks, analog computation in general suffers from non-idealities and systematic errors in the circuitry, which could degrade the performance and accuracy significantly. One of the issues is the random offset associated with the op-amps in the system resulting from the process and mismatch variations. In this paper, a novel technique is offered to reduce the negative effects of the random offset and increase the output accuracy. This newly proposed system uses minimum extra circuitry and additional power consumption and only requires the crossbar to be enlarged by two extra rows. The intrinsic issue of the analog crossbars, interconnect parasitics, must be incorporated into the problem, and a way to separate the offset and wire resistance issues from each other is offered. The functionality of the system has been shown with a case study in the results section where the op-amps have so sigma(offset) = 3mV. The effectiveness of the offered technique demonstrates a 6x better accuracy with the mitigation of the offset problem. The proposed method can be used in memristor and other analog crossbars to achieve a greater performance and thus improve their competitiveness.