Real-time classification of objects from active sonar echo-location requires a tremendous amount of computation, yet bats and dolphins perform this task effortlessly. To bridge the gap between human-engineered and biosonar system performance, we developed special-purpose hardware tailored to the parallel distributed nature of the computation performed in biology. The implemented architecture contains a cochlear filterbank front-end performing time-frequency feature extraction, and a kernel-based neural classifier for object detection. Based on analog programmable components, the front-end can be configured as a parallel or cascaded bandpass filterbank of up to 34 channels spanning the 10 to 150 kHz range. The classifier is implemented with the Kerneltron, a massively parallel mixed-signal Support Vector “Machine” in silicon delivering a throughput in excess of a trillion (1012) multiply-accumulates per second for every Watt of power dissipation. The system has been evaluated on detection of mine-like objects using linear frequency modulation active sonar data (LFM2, CSS Panamy City), achieving an out-or-sample performance of 93% correct single-ping detection at 5% false positives, and a real-time throughput of 250 pings per second.

Field-programmable gate arrays (FPGAs) have been a great boon to the electronics industry, facilitating rapid and reliable prototyping of digital systems at low cost. Radiation-tolerant versions have had a major impact on space industries where low volume and the requirement of flight qualification makes custom ASIC design an expensive option, with slow development cycles. Presently, custom ASIC or discrete component design are the usual options for analog and mixed-signal circuit design, FPAA (Field-Programmable Analog Array) and FPMA (Field-Programmable Mixed-signal Array) parts exist but tend to be rather specialized in function, and none exist in radiation-tolerant form for use in aerospace applications. This paper outlines an architecture proposed for an FPAA fabricated in an existing commercial digital CMOS process used to make radiation-tolerant antifuse-based FPGA devices. The primary concerns are the impact of the technology and the overall array architecture on the flexibility of programming, the bandwidth available for high-speed analog circuits, and the accuracy of the components for high-performance applications.

Phase distortion in wavefront propagation is one of the key problems in optical imaging and laser optics applications. We present a hybrid VLSI and optical system for real-time adaptive phase distortion compensation. The system operates "model-free", independent of the specifics of the distorting optical medium and the compensation control elements. Our VLSI system implements stochastic parallel perturbative gradient descent/ascent so that we achieve fast optimization of the chosen performance metric to achieve real-time compensation. We include experimental results of the hybrid VLSI-optical system demonstrating successful operation for a laser-beam focusing/defocusing task.

I present an architecture for acoustic pattern classification using trinary-trinary template correlation. In spite of its computational simplicity, the algorithm and architecture represent a method which greatly reduces bandwidth of the input, storage requirements of the classifier memory, and power consumption of the system without compromising classification accuracy. The linear system should be amenable to training using recently-developed methods such as Independent Component Analysis (ICA), and we predict that behavior will be qualitatively similar to that of structures in the auditory cortex.