  On-device intelligence is gaining significant attention recently as it offers
local data processing and low power consumption. In this research, an on-device
training circuitry for threshold-current memristors integrated in a crossbar
structure is proposed. Furthermore, alternate approaches of mapping the
synaptic weights into fully-trained and semi-trained crossbars are
investigated. In a semi-trained crossbar a confined subset of memristors are
tuned and the remaining subset of memristors are not programmed. This
translates to optimal resource utilization and power consumption, compared to a
fully programmed crossbar. The semi-trained crossbar architecture is applicable
to a broad class of neural networks. System level verification is performed
with an extreme learning machine for binomial and multinomial classification.
The total power for a single 4x4 layer network, when implemented in IBM 65nm
node, is estimated to be ~ 42.16uW and the area is estimated to be 26.48um x
22.35um.
