  In this paper, we first propose the design of Temporal-Carry-deferring MAC
(TCD-MAC) and illustrate how our proposed solution can gain significant energy
and performance benefit when utilized to process a stream of input data. We
then propose using the TCD-MAC to build a reconfigurable, high speed, and low
power Neural Processing Engine (TCD-NPE). We, further, propose a novel
scheduler that lists the sequence of needed processing events to process an MLP
model in the least number of computational rounds in our proposed TCD-NPE. We
illustrate that our proposed TCD-NPE significantly outperform similar neural
processing solutions that use conventional MACs in terms of both energy
consumption and execution time.
