Quantizer neuron model and neuroprocessor-named quantizer neuron chip

A quantizer neuron model and a hardware implementation of the model is described. A quantizer neuron model and a multifunctional layered network (MFLN) with quantizer neurons is proposed and applied to a character recognition system. Each layer of MFLN has a specific function defined by quantizer input, and the weights between neurons are set dynamically according to quantizer inputs. The learning speed of MFLN is extremely fast in comparison with conventional multilayered perceptrons using back propagation, and the structure of MFLN is suitable for supplemental learning with extraneous learning data sets. We tested the learning speed and compared it with three other network models: RCE networks, LVQ3, and multilayered neural network with back propagation. According to the simulation, we also developed a quantizer neuron chip (QNC) using two newly developed schemes. QNC simulates MFLN and has 4736 neurons and 2000000 synaptic weights. The processing speed of the chip achieved 20300000000 connections per second (GCPS) for recognition and 20 000 000 connection updates per second (MCUPS) for learning. QNC is implemented in a 1.2 μm double-metal CMOS-process sea of gates and contains 27 000 gates on a 10.99×10.93 mm² die. The neuroboard, which consists of a main board with a QNC and a memory board for synaptic weights of the neurons, can be connected to a host personal computer and can be used for image or character recognition and learning. The quantizer neuron model, the quantizer neuron chip, and the neuroboard with QNC can realize adaptive learning or filtering