A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems

Data-intensive computing applications such as object recognition, time series prediction and optimization tasks are becoming increasingly important in several fields including smart mobility, health and industry. Because of the large amount of data involved in the computation, the conventional von Neumann architecture suffers from excessive latency and energy consumption due to the memory bottleneck. A more efficient approach consists of inmemory computing (IMC), where computational operations are directly carried out within the data. IMC can take advantage of the rich physics of memory devices, such as their ability to store analogue values to be used in matrix-vector multiplication (MVM) and their stochasticity which is highly valuable in the frame of optimization and constraint satisfaction problems (CSPs). This work presents a stochastic spiking neuron based on a phase change memory (PCM) device for the solution of CSPs within a Hopfield recurrent neural network (RNN). In the RNN, the PCM cell is used as the integrating element of a stochastic neuron, supporting the solution of a typical CSP, namely a Sudoku puzzle in hardware. Finally, the ability to solve Sudoku puzzles using RNNs with PCM-based neurons is studied for increasing size of Sudoku puzzle by a compact simulation model, thus supporting our PCM-based RNN for data-intensive computing.