Impact of Synaptic Device Variations on Pattern Recognition Accuracy in a Hardware Neural Network.

ABSTRACT: Neuromorphic systems (hardware neural networks) derive inspiration from biological neural systems and are expected to be a computing breakthrough beyond conventional von Neumann architecture. Interestingly, in neuromorphic systems, the processing and storing of information can be performed simultaneously by modulating the connection strength of a synaptic device (i.e., synaptic weight). Previously investigated synaptic devices can emulate the functionality of biological synapses successfully by utilizing various nano-electronic phenomena; however, the impact of intrinsic synaptic device variability on the system performance has not yet been studied. Here, we perform a device-to-system level simulation of different synaptic device variation parameters in a designed neuromorphic system that

SUBMITTER: Kim S 

PROVIDER: S-EPMC5805704 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature