SNRA: A Spintronic Neuromorphic Reconfigurable Array for In-Circuit Training and Evaluation of Deep Belief Networks

Abstract

In this paper, a spintronic neuromorphic reconfigurable Array (SNRA) is developed to fuse together power-efficient probabilistic and in-field programmable deterministic computing during both training and evaluation phases of restricted Boltzmann machines (RBMs). First, probabilistic spin logic devices are used to develop an RBM realization which is adapted to construct deep belief networks (DBNs) having one to three hidden layers of size 10 to 800 neurons each. Second, we design a hardware implementation for the contrastive divergence (CD) algorithm using a four-state finite state machine capable of unsupervised training in N+3 clocks where N denotes the number of neurons in each RBM. The functionality of our proposed CD hardware implementation is validated using ModelSim simulations. We synthesize the developed Verilog HDL implementation of our proposed test/train control circuitry for various DBN topologies where the maximal RBM dimensions yield resource utilization ranging from 51 to 2,421 lookup tables (LUTs). Next, we leverage spin Hall effect (SHE)-magnetic tunnel junction (MTJ) based non-volatile LUTs circuits as an alternative for static random access memory (SRAM)-based LUTs storing the deterministic logic configuration to form a reconfigurable fabric. Finally, we compare the performance of our proposed SNRA with SRAM-based configurable fabrics focusing on the area and power consumption induced by the LUTs used to implement both CD and evaluation modes. The results obtained indicate more than 80% reduction in combined dynamic and static power dissipation, while achieving at least 50% reduction in device count.