There are two dynamics issues in vision chips: (i) The temporal dynamics issue due to the parasitic capacitors in a CMOS chip, and (ii) the spatial dynamics issue due to the regular array of processing elements in a chip. These issues are discussed in [1]-[3] for the resistor network with only associated parasitic capacitances. However, in this paper we consider also parasitic inductances as well as parasitic capacitances for a more precise network dynamics model. We show that in some cases the temporal stability condition for the network with parasitic inductances and capacitances is equivalent to that for the network with only parasitic capacitances, but in general they are not equivalent. We also show that the spatial stability conditions are equivalent in both cases.

A digital neuro chip with proliferating neuron architecture is described. This chip simulates a neural network model called the adaptive segmentation of quantizer neuron architecture (ASQA). It has proliferating neurons, and can automatically form the optimum network structure for recognition according to the input data. To develop inexpensive commercial hardware and implement a proliferating neuron architecture, we adopt a virtual neuron system for hardware implementation. Namely, this chip is implemented with only an arithmetic unit for network computations, and the network information such as network structure, synaptic weights and so on, are stored in external memories. We devise our original architecture which can efficiently memorize the network information, and moreover, construct a structured network using the ASQA model. As a result, we can recognize about 3,000 Kanji characters using a single chip and a recognition speed of 4.6 msec/character is achieved on a PC.

We developed programmable high-performance and high-speed neurocomputer for a large neural network using ASIC neurocomputing chips made by CMOS VLSI technology. The neurocomputer consists of one master node and multiple slave nodes which are connected by two data paths, a broadcast bus and a ring bus. The nodes are made by ASIC chips and each chip has plural nodes in it. The node has four types of computation hardware that can be cascaded in series forming a pipeline. Processing speed is proportional to the number of nodes. The neurocomputer is built on one printed circuit board having 65 VLSI chips that offers 1.5 billion connections/sec. The neurocomputer uses SIMD for easy programming and simple hardware. It can execute complicated computations, memory access and memory address control, and data paths control in a single instruction and in a single time step using the pipeline. The neurocomputer processes forward and backward calculations of multilayer perceptron type neural networks, LVQ, feedback type neural networks such as Hopfield model, and any other types by programming. To compute neural computation effectively and simply in a SIMD type neurocomputer, new processing methods are proposed for parallel computation such as delayed instruction execution, and reconfiguration.