Impact of Nonideal Resistive Synaptic Device Behaviors on Implementation of Sparse Coding Algorithm

The sparse coding is an unsupervised algorithm that can efficiently extract the features from the input dataset. However, the learning accuracy can be potentially hampered by several limiting factors of the resistive synaptic device behaviors, including the nonlinearity and device variations in weight update, and the read noise, limited ON/OFF weight ratio and array parasitics in weighted sum. This book chapter employs device-algorithm co-design methodologies to quantify and mitigate the impact of these nonideal properties on the learning accuracy at the system level. It is observed that the realistic device behaviors in the weight update are tolerable, while those in the weighted sum are detrimental to the accuracy. The strategies to mitigate this accuracy loss include (1) multiple cells as a synapse to alleviate the impact of device variations, (2) a dummy column at minimum conductance to eliminate the off-state current, and (3) selector and larger array wire width to reduce IR drop along interconnects.