This paper proposes a masked data transferring method for the write-back controlled disk cache system employing a fixed-length recording disk drive, enabling data transfer of discontinuous sectors on the same track between the cache and the disk. This paper also evaluates the method. In write-back controlled disk cache sytems, random write requests cause dirty data (write-pending data on a cache) on discontinuous areas on the cache. It is likely that several sectors on the same track become dirty. These dirty sectors must be written onto the disk according to the cache management scheme. In conventional data transferring methods between a disk cache and a disk drive, plural sectors can be transferred in one single operation when the sectors are adjacent, but discrete sectors must be transferred by individual operations. In the methods, an address of the head sector and number of sectors to be transferred are given to the transfer unit. For example, when two sectors on the same track are located closely but not adjacently, and data transfer is requested for those two sectors, the transfer operation for the second sector must be prepared after the first transfer had completed and before the second sector arrives under the disk head. Although the time for the head to pass by the uninterested sector is often too short for the software overhead for the first transfer to be completed and the second transfer to be prepared, which leads to an unwanted extra rotation of the disk. With the masked transferring method proposed in this paper, the micro program creates a bit-map specifying the target sectors to be transferred and passes it to the data transfer unit, enabling to transfer the discontinuous sectors without latency. The method was evaluated using OLTP warkloads. Results show an improvement in random I/O throughput of between 8% and 27%. The masked transferring method is adopted in Hitachi's A-6521 disk subsytems, shipped since December 1993.

We propose an asymmetric neural network which can solve inequality-constrained combinatorial optimization problems that are difficult to solve using symmetric neural networks. In this article, a knapsack problem that is one of such the problem is solved using the proposed network. Additionally, we study condition for obtaining a valid solution. In computer simulations, we show that the condition is correct and that the proposed network produces better solutions than the simple greedy algorithm.

The Traveling Salesman Problem (TSP) can be solved by a neural network using the coding scheme based on the adjacency of city in the tour. Using this coding scheme, the neural network generates a better solution than that using other coding schemes. We, however, often get the invalid solution consisting of some subtours. In this article, we propose a method of eliminating subtours using additional neurons. On the computer simulation it is shown that we get the optimum solution by means of taking only O(n2) additional neurons and trials.

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.