Open AccessWeight Update Generation Circuit Utilizing Phase Noise of Integrated Complementary Metal–Oxide–Semiconductor Ring Oscillator for Memristor Crossbar Array Neural Network‐Based Stochastic Learning
Author(s) -
Bae Woorham,
Yoon Kyung Jean
Publication year - 2020
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202000011
Subject(s) - dither , ring oscillator , cmos , memristor , artificial neural network , computer science , phase noise , crossbar switch , electronic engineering , control theory (sociology) , algorithm , engineering , artificial intelligence , noise shaping , control (management)
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.