Coarse Grained Reconfigurable Architectures
(CGRAs) are emerging as enabling platforms to meet the high
performance demanded by modern embedded applications. In
many application domains (e.g. robotics and cognitive embedded
systems), the CGRAs are required to simultaneously host
processing (e.g. Audio/video acquisition) and estimation (e.g.
audio/video/image recognition) tasks. Recent works have revealed
that the efficiency and scalability of the estimation algorithms
can be significantly improved by using neural networks.
However, existing CGRAs commonly employ homogeneous
processing resources for both the tasks. To realize the best of
both the worlds (conventional processing and neural networks),
we present FIST. FIST allows the processing elements and the
network to dynamically morph into either conventional CGRA
or a neural network, depending on the hosted application. We
have chosen the DRRA as a vehicle to study the feasibility and
overheads of our approach. Synthesis results reveal that the
proposed enhancements incur negligible overheads (4.4% area
and 9.1% power) compared to the original DRRA cell.