  As cost and performance benefits associated with Moore's Law scaling slow,
researchers are studying alternative architectures (e.g., based on analog
and/or spiking circuits) and/or computational models (e.g., convolutional and
recurrent neural networks) to perform application-level tasks faster, more
energy efficiently, and/or more accurately. We investigate cellular neural
network (CeNN)-based co-processors at the application-level for these metrics.
While it is well-known that CeNNs can be well-suited for spatio-temporal
information processing, few (if any) studies have quantified the
energy/delay/accuracy of a CeNN-friendly algorithm and compared the CeNN-based
approach to the best von Neumann algorithm at the application level. We present
an evaluation framework for such studies. As a case study, a CeNN-friendly
target-tracking algorithm was developed and mapped to an array architecture
developed in conjunction with the algorithm. We compare the energy, delay, and
accuracy of our architecture/algorithm (assuming all overheads) to the most
accurate von Neumann algorithm (Struck). Von Neumann CPU data is measured on an
Intel i5 chip. The CeNN approach is capable of matching the accuracy of Struck,
and can offer approximately 1000x improvements in energy-delay product.
