A VLSI neural processor for image data compression using self-organization networks

An adaptive electronic neural network processor has been developed for high-speed image compression based on a frequency-sensitive self-organization algorithm. The performance of this self-organization network and that of a conventional algorithm for vector quantization are compared. The proposed method is quite efficient and can achieve near-optimal results. The neural network processor includes a pipelined codebook generator and a paralleled vector quantizer, which obtains a time complexity O(1) for each quantization vector. A mixed-signal design technique with analog circuitry to perform neural computation and digital circuitry to process multiple-bit address information are used. A prototype chip for a 25-D adaptive vector quantizer of 64 code words was designed, fabricated, and tested. It occupies a silicon area of 4.6 mmx6.8 mm in a 2.0 mum scalable CMOS technology and provides a computing capability as high as 3.2 billion connections/s. The experimental results for the chip and the winner-take-all circuit test structure are presented.