Spin Torque Nano-Oscillator Based Oscillatory Neural Network

Oscillatory Neural Networks (ONN) are becoming a popular neuromorphic computing model owing to their efficient parallel processing capabilities. Hoppensteadt and Izhikevich proposed an ONN architecture resembling associative memory, with Phase-Locked Loop (PLL) circuits as neurons. Unfortunately, there are shortcomings in realizing such architectures due to the inefficiencies of CMOS based implementations of oscillators and other hardware. We propose a PLL structure for ONN applications fashioned using energy efficient and scalable Spin Torque Oscillators (STOs). We demonstrate the functionality of a 60 neuron ONN using STOs for binary image identification