  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 design, the cellular
memristive-output reservoir (CMOR), is implemented in 65-nm CMOS technology
with integrated front-end-of-the-line (FEOL) resistive random-access memory
(ReRAM) used to construct a trainable output layer. We detail the design of
this system and present electrical test results verifying its operation and
capability to carry out non-linear classifications.
