Kerneltron: Implantable Pattern Recognition Processor

Detection of complex objects in streaming video requires a significant amount of computation by the classifier operating in real time. This computation is dominated by the evaluation of a kernel between an input vector and a set of stored templates. For a large class of kernels the computation involves a matrix-vector multiplication (MVM) in large dimensions. For complex pattern recognition tasks this requires millions of matrix multiply-accumulates for each classification per frame of the streaming video. To handle this task, a pattern recognition processor, the Kerneltron, has been prototyped in a 0.5 um CMOS technology. This massively parallel processor combines a fine grain analog computational array with oversampling quantizers to efficiently compute the MVM. The goal of my work has been twofold. First, a new generation Kerneltron-family processor has been designed. Second, I have further enhanced the Kerneltron technology through cell-level and chip-level circuit innovations.

The first part of the project involved prototyping a large-scale version of the Kerneltron processor with an order of magnitude improvement in the computational efficiency (throughput per unit power) over the previous prototype. On a system level, a multiprocessor PCI-based PC card containing 8 new-generation Kerneltron processors has been developed and prototyped. The multi-chip system will be demonstrated in real time object detection in streaming video.

The second part of the project has been to design, prototype, and experimentally characterize a new architecture with circuit innovations aimed at further improvement in computational efficiency. The objective was to design a denser cell operating with a smaller supply voltage. This processor will be used to demonstrate the enhanced potential of the computing technology.

The finished project is a Kerneltron based system for object detection in streaming video. The high computational efficiency of the Kerneltron technology is ideal for portable, low-power applications.