Efficient Training of Neural Gas Vector Quantizers with Analog Circuit Implementation

This paper presents an algorithm for training vector quantizers with an improved version of the Neural Gas model, and its implementation in analog circuitry. Theoretical properties of the algorithm are proven that clarify the performance of the method in terms of quantization quality, and motivate design aspects of the hardware implementation. The architecture for vector quantization training includes two chips, one for Euclidean distance computation, the other for programmable sorting of codevectors. Experimental results obtained in a real application (image coding) support both the algorithm’s effectiveness and the hardware performance, which can speed up the training process by up to two orders of magnitude.