Cellular Memristive-Output Reservoir (CMOR)
Wilkie Olin-Ammentorp, Karsten Beckmann, Nathaniel C. Cady

TL;DR
This paper presents the design, fabrication, and testing of a fully integrated CMOS-based reservoir computing circuit called CMOR, utilizing cellular automata and memristive memory for non-linear classification tasks.
Contribution
It introduces a novel monolithic reservoir computing circuit with integrated programmable read-out, combining cellular automata and ReRAM in 65-nm CMOS technology.
Findings
Successful electrical testing of the CMOR circuit
Verification of non-linear classification capability
Demonstration of integrated, programmable read-out layer
Abstract
Reservoir computing is a subfield of machine learning in which a complex system, or 'reservoir,' uses complex internal dynamics to non-linearly project an input into a higher-dimensional space. A single trainable output layer then inspects this high-dimensional space for features relevant to perform the given task, such as a classification. Initially, reservoirs were often constructed from recurrent neural networks, but reservoirs constructed from many different elements have been demonstrated. Elementary cellular automata (CA) are one such system which have recently been demonstrated as a powerful and efficient basis which can be used to construct a reservoir. To investigate the feasibility and performance of a monolithic reservoir computing circuit with a fully integrated, programmable read-out layer, we designed, fabricated, and tested a full-custom reservoir computing circuit. This…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Advanced Memory and Neural Computing · Neural dynamics and brain function
