Leveraging stochasticity in memristive synapses for efficient and reliable neuromorphic systems

Neuromorphic computing is inspired by the human brain’s architecture to develop power-efficient and optimized neural networks. Different characteristics of synapses and neurons are emulated in the neuromorphic hardware or software model to mimic the behavior of the synaptic brain. Noise in the neuromorphic system is an important constraint to evaluate its overall performance. A reliable READ operation is essential to secure an acceptable performance from a neuromorphic system. In addition, a memristive synapse is very prone to stochastic behavior. Moreover, a dynamically reconfigurable READ operation is proposed for our 3T1R synapse to explore the effect of stochasticity on neuromorphic applications. Our proposed method allows for relevant applications in neuromorphic computing to utilize the stochastic behavior of the memristive 3T1R synapse. Performance evaluations show ~8.9x energy optimization with our proposed device. Optimization algorithm (EONS) is utilized for training and a hardware framework (RAVENS) is used for hardware simulation during training.