Weight Update Generation Circuit Utilizing Phase Noise of Integrated Complementary Metal–Oxide–Semiconductor Ring Oscillator for Memristor Crossbar Array Neural Network‐Based Stochastic Learning

Abstract

Herein, a robust programmable stochastic weight generation method for a memristive neural network is proposed. There have been few prior algorithm suggestions for crossbar neural network‐based stochastic learning; however, there has not been much attention focussed on robust physical implementations. As a result, coming up with a robust method to provide the probability generator is an essential knob for its physical implementation. Here, implanting such stochastic behavior into the weight update signal itself is proposed, by multiplying it with the randomized probability sequence. To generate such probability sequence, bang‐bang dithering of a phase‐locked loop (PLL) with a binary phase detector (PD) is used. The programmable probability is enabled by introducing an offset for the PD outputs. Yet the dithering sequence has deterministic nature, phase noise of complementary metal–oxide–semiconductor (CMOS) ring oscillator to randomize the deterministic dithering is exploited. As a result, this lower power oscillator offers a better probability sequence, which enables an ultralow power circuit implementation.

Keywords

• crossbar neural networks
• memristors
• oscillator phase noise
• stochastic learning
• weight generation circuits