A 0.93 nW/node Ultra-Low Power Oscillatory Neural Network using BTBT-based Oscillators

Combinatorial optimization problems (COPs), when addressed using traditional von Neumann computers, demand significant computational power and substantial area as problem dimensionality increases. Hardware-based solvers, particularly those employing coupled oscillator networks to mimic Ising machines, have been explored as alternatives. However, conventional CMOS-based solutions face limitations in terms of power consumption and area. In this work, we propose a low-power 8-node oscillatory neural network using a band-to-band-tunneling-based ring oscillator in GF45RFSOI technology. This ultra-low power and low-area design efficiently solves COPs without an external perturbation signal. We use the intrinsic noise of BTBT-based oscillators to augment the phase synchronization among the coupled oscillators. The proposed system allows configurable all-to-all connectivity between ring oscillator nodes through cross-coupled capacitors. We demonstrate the system’s efficacy in solving multiple vector graph coloring problems, achieving an average power consumption of 0.93 nW (10⁵× lower than state-of-the-art) per oscillator with a supply voltage of 1.8 V.