Design-technology co-optimization for OxRRAM-based synaptic processing unit

• Published in: 2017 Symposium on VLSI Technology pp. T178 - T179
• Main Authors: Mallik, A., Garbin, D., Fantini, A., Rodopoulos, D., Degraeve, R., Stuijt, J., Das, A. K., Schaafsma, S., Debacker, P., Donadio, G., Hody, H., Goux, L., Kar, G. S., Furnemont, A., Mocuta, A., Raghavan, P.
• Format: Conference Proceeding
• Language: English
• Published: JSAP 01.06.2017
• Subjects: Arrays; Classification algorithms; Distortion; Encoding; Measurement; Neuromorphics; Software algorithms
• DOI: 10.23919/VLSIT.2017.7998166

In this paper, we present a design-technology tradeoff analysis to implement a fully connected neural network using non-volatile OxRRAM cells. The requirement of a high number of distinct levels in synaptic weight has been established as a primary bottleneck for using a single NVM as a synaptic unit. We propose a mixed-radix encoding system for a multi-device synaptic unit achieving high classification accuracy (94%) including device variability. To our knowledge, this is the first paper to discuss the tradeoff between single and multi-device synaptic weight in terms of design and technology using silicon data. We have demonstrated that high level of variability can be handled by the neuromorphic algorithm. The results presented in the paper has been obtained from 1Mb array.