Real-time analog processing with on-chip learning using multiple-input translinear elements

Analog processing has re-emerged as a mode of computing complex real-time algorithms since it consumes less power. Analog circuits are susceptible to mismatches, and onboard training could mitigate variations. In this study, we are presenting the use of analog floating-gate MITE-based circuits for various computations. Analog computation can provide a significant advantage in applications where signals are in the analog domain. The study uses a neural decoding task to demonstrate an application where input neural data are mapped to kinematics. The study demonstrates a real-time neural decoding task with an analog adaptive circuit. The on-chip learning algorithm is developed to adapt the parameters of the analog adaptive circuit. On-chip learning improves the overall Pearson correlation coefficient from 0.07 to 0.69 for a neural decoding task. On-chip learning and adaptation can significantly reduce the need for off-chip communication in implantable devices.