M. Maruyama et al., QUANTIZER NEURON CHIP (QNC) WITH MULTICHIP EXTENDIBLE ARCHITECTURE, IEICE transactions on electronics, E77C(7), 1994, pp. 1057-1064
This paper discusses a digital neuroprocessor named Quantizer Neuron C
hip (QNC) employing the Quantizer Neuron model and two newly developed
schemes; ''concurrent processing of quantizer neuron'' and ''removal
of ineffective calculations''. QNC simulates neural networks named the
Multi-Functional Layered Network (MFLN) with 64 output neurons, 4672
quantizer neurons and two million synaptic weights and can be used for
character or image recognition and learning. The processing speed of
the chip achieved 1.6 mu seconds per output neuron for recognition and
20 million connections updated per second (MCUPS) for learning. In ad
dition, QNC can execute multichip operation for increasing the size of
networks. We applied QNC to handwritten numeral recognition and reali
zed high speed recognition and learning. QNC is implemented in a 1.2 m
um double metal CMOS with sea of gates' technology and contains 27,000
gates on a 10.99 X 10.93 mm2 chip.