Toshiyuki Furuta

Neural network LSI chip with on-chip learning

A model for neural network learning and recall has been developed and implemented in digital LSI. Activation, weight, and error signals are represented by stochastic digital pulse trains. The average pulse frequency is the value of the signal. All mathematical operations are performed in parallel using simple logical operations on the signal pulses. Learning is performed on the chip. A network of these artificial neural networks rapidly learned the solution to a two-dimensional inverted pendulum-balancer control problem. Another such network solved a simple character recognition problem.<<ETX>>

A digital neural network VLSI with on-chip learning using stochastic pulse encoding

A digital neural network VLSI chip, RN200 has been developed and fabricated. Sixteen neurons and totally 256 synapses are integrated in a 13.73/spl times/13.73 mm/sup 2/ VLSI chip, fabricated by RICOH 0.8 /spl mu/m CMOS technology. Multiple-layer neural network can be made by combining two or more-chips. Signals within the network (e.g., activations, error signals, connection weights) are represented by stochastic digital pulse trains. Both feed forward and learning processes are efficiently implemented with simple logical gates. Our novel approach for approximating the derivative of activation function is described. The approximation circuit requires only a few gates. Multiple-RNG architecture is adopted to ensure the random distribution of pulses. Both seeds and configurations of the random number generators on the chip can be updated dynamically and randomly by this mechanism. The effectiveness of the derivative and the Multiple-RNG architecture are simulated and verified with the learning performance in a hand-written character recognition problem. The chip can perform 5.12 gigapulse operations per second. It corresponds to effective neural computing rate of 40M CPS or 40M CUPS.

Hardware-based neural identification: linear dynamical systems

In this paper, a hardware-based neural identification method is proposed in order to learn the characteristics or structure of a discrete linear dynamical system. Quick or instant identification of unknown dynamical systems is particularly required for practical controls not only in intelligent mechatronics such as, for example, automatic selforganized running of mobile vehicles, but in intelligent self-controlled systems. We developed a new method of hardware-based identification for general dynamical systems using a digital neural network very large scale integration (VLSI) chip, RN-200, where sixteen neurons and a total of 256 synapses are integrated in a 13.73×13.73 mm2 VLSI chip, fabricated using RICOH 0.8 μm complementary metal oxide semiconductor CMOS technology (RICOH, Yokohama, Japan). This paper describes how to implement neural ideitification in both learning and feedfoward processing (recognizing) using a RICOH RN-2000 neurocomputer which consists of seven RN-200 digital neural network VLSI chips.

Neural networks for control in an artificial brain of a recognition and tracking system

This paper presents a new information-processing machine which is called the artificial brain (ABrain). It also considers the structure of artificial neural networks constructed in a Ricoh neurocomputer RN-2000 in the ABrain to track given trajectories which are produced in a micro-computer or by a light moved by hand in a recognition and tracking system.

Cloud-based Hint Application of Relevant Meeting Materials Using OCR and ASR Text Information

In the meeting being held, sometimes we need to confirm a lot of past relevant meeting materials. However, as the number of meetings has become larger, the retrieval of relevant meeting materials has become much more difficult. Whats more, the retrieval action should not interrupt the meeting process unless on participants own intention. Therefore, during the meeting, an efficient hint method for essential meeting materials without interrupting meeting process is urgently needed. In this paper, we provide a cloud-based application for real-time meeting materials hint in the meeting being held.