High-resolution synaptic weights and hardware-in-the-loop learning

摘要

Abstract: Artificial neural network paradigms are derived from biological nervous system and are characterized by massive parallelism. These networks have shown the capabilities of processing input-output mapping operations even where the transformation rules are not known, partially known, or ill-defined. For high-speed processing, we have fabricated neural network architectures as building-block chips with either a 32 $MUL 32 matrix of synapses or a 32 $MUL 31 array of synapses along with 32 neurons along a diagonal for a 32 $MUL 32 matrix. Reconfigurability allows a variety of architectures from fully recurrent to fully feedforward, including constructive architectures such as cascade correlation. Further, a variety of gradient-descent learning algorithms have been implemented. Additionally, the chips being cascadable, larger size networks are easily assembled. An innovative scheme of combining two identical synapses on two respective chips in parallel nominally doubles the bit resolution from 7 bits (6-bit $PLU sign) to 13 bits (12-bit $PLU sign). We describe the feedforward net obtained by assembly of 8 chips on a board with nominally 13 bits of resolution for a hardware-in-the-loop learning of a feature classification problem involving map-data. This neural net hardware with 27 analog inputs and 7 outputs is able to learn to classify the features and provide the required output map at high speed with 89% accuracy. This result, with hardware's lower precision, etc., compares favorably with an accuracy of 92% obtained both by a neural network software simulation (floating point accuracy of synaptic weights) and a statistical technique of k-nearest neighbors. !21