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

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...

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Bibliographic Details
Published in2017 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
LanguageEnglish
Published JSAP 01.06.2017
Subjects
Arrays
Classification algorithms
Distortion
Encoding
Measurement
Neuromorphics
Software algorithms
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Summary: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.
ISSN:2158-9682
DOI:10.23919/VLSIT.2017.7998166