A stochastic learning algorithm for neuromemristive systems

In this paper, we present a stochastic learning algorithm for neuromemristive systems. Existing algorithms are based on gradient descent techniques, which require analog multiplications. The proposed algorithm removes the necessity for an analog multiplier by transforming each variable into a random Bernoulli-distributed value. Arithmetic operations on such values are easily implemented using digital circuits, reducing the area cost of the implementation. We tested the proposed algorithm and compared with the least-mean-squares algorithm on both linear and logistic regression problems. Results indicate that the proposed algorithm is able to achieve similar accuracy with up to ≈3.5× less area.