  Random device mismatch that arises as a result of scaling of the CMOS
(complementary metal-oxide semi-conductor) technology into the deep submicron
regime degrades the accuracy of analogue circuits. Methods to combat this
increase the complexity of design. We have developed a novel neuromorphic
system called a Trainable Analogue Block (TAB), which exploits device mismatch
as a means for random projections of the input to a higher dimensional space.
The TAB framework is inspired by the principles of neural population coding
operating in the biological nervous system. Three neuronal layers, namely
input, hidden, and output, constitute the TAB framework, with the number of
hidden layer neurons far exceeding the input layer neurons. Here, we present
measurement results of the first prototype TAB chip built using a 65nm process
technology and show its learning capability for various regression tasks. Our
TAB chip exploits inherent randomness and variability arising due to the
fabrication process to perform various learning tasks. Additionally, we
characterise each neuron and discuss the statistical variability of its tuning
curve that arises due to random device mismatch, a desirable property for the
learning capability of the TAB. We also discuss the effect of the number of
hidden neurons and the resolution of output weights on the accuracy of the
learning capability of the TAB.
