TEMPORAL SIGNAL-PROCESSING WITH HIGH-SPEED HYBRID ANALOG-DIGITAL NEURAL NETWORKS

There are many problems which fall into the class of temporal signal processing. These problems have in common the need to relate the temporal properties of their inputs. Conventional solutions to these problems often have high hardware overhead, complex algorithmic solutions, or loss of information through the transformation of temporal properties of the input. To this end, a biologically motivated artificial and neural processing element has been developed. As in biological neurons, processing is time dependent and is implemented using both analog and digital techniques. These characteristics make the PE directly applicable a large class of temporal signal processing problems typically encountered in engineering and science. Multiple aspects of the PE behavior are adjustable, which produces a very wide range of behaviors from simple systems with only a few moderately connected processing elements. The processing element models are custom designed electric circuits based on basic CMOS components and therefore all developed systems can be directly implemented in any standard integrated CMOS technology. The integrated implementation, custom design, and a wide range of adaptable behaviors join to produce a very fast, low-hardware solution to complex spatiotemporal signal processing problems. Seven novel systems based on the hybrid PE are discussed as they relate to commonly encountered temporal signal processing problems.