  Temporal Neural Networks (TNNs), inspired from the mammalian neocortex,
exhibit energy-efficient online sensory processing capabilities. Recent works
have proposed a microarchitecture framework for implementing TNNs and
demonstrated competitive performance on vision and time-series applications.
Building on these previous works, this work proposes TNN7, a suite of nine
highly optimized custom macros developed using a predictive 7nm Process Design
Kit (PDK), to enhance the efficiency, modularity and flexibility of the TNN
design framework. TNN prototypes for two applications are used for evaluation
of TNN7. An unsupervised time-series clustering TNN delivering competitive
performance can be implemented within 40 uW power and 0.05 mm^2 area, while a
4-layer TNN that achieves an MNIST error rate of 1% consumes only 18 mW and
24.63 mm^2. On average, the proposed macros reduce power, delay, area, and
energy-delay product by 14%, 16%, 28%, and 45%, respectively. Furthermore,
employing TNN7 significantly reduces the synthesis runtime of TNN designs (by
more than 3x), allowing for highly-scaled TNN implementations to be realized.
