VLSI computing arrays for matrix multiplication and covariance matrix inversion have applications in many fields. Under the constraint of limited I/O bandwidth of the host system or the computing array, three configurations for the interfacing and controlling of a multiplication array to achieve optimal performance under different adverse situations are examined. The three configurations are multiplexing loading, processor row loading, and processor column group loading. A properly chosen configuration can significantly reduce the computing time of the multiplication array.

Binary random variables are regarded as random vectors in a binary-field (modulo-2) linear vector space. A characteristic function is defined and related results derived using this formulation. Minimax estimation of probability distributions using an entropy criterion is investigated, which leads to an A-distribution and bilinear discriminant functions. Nonparametric classification approaches using Hamming distances and their asymptotic properties are discussed. Experimental results are presented.

The paper is concerned with the reduction of overhead storage, i.e., the stored compression/decompression (C/D) table, in field-level data file compression. A large C/D table can occupy a lage fraction of maim memory space during compression and decompression, and may cause excessve page swapping in virtual memory systems. A two-stage approach is studied, including the reuired additional C/D table decompression time. It appears that the approach has limitations and is not completely satisfactory.

For the compression of data fields in the records of a data file, the fixed-length minimum bit (FLMB) method is commonly used. The scheme re quires a compression/decompression (C/D) table which could be very large in size. A large C/D table can occupy a large fraction of the main memory space, and it may cause excessive page swaping in virtual memory systems. To reduce the C/D table overhead storage, a two-stage data compression approach is studied in which several methods are used to further compress the C/D table itself. The resulting C/D table decompression time during the regular data decompression process is also examined.

The objective of the present paper is to investigate possible methods to reduce a program's execution time by detecting and converting automatically the more frequently executed program parts, mostly inner loops, into microcode.

A nonsingular linear transformation of binary-valued random vectorsy = xAwhich minimizes a mutual information criterion I(y) is considered. It is shown that a nonsingular A exists such thatI(y) = 0if and only ifxhas a generalized binomial distribution. Computational algorithms for seeking an optimalAare developed, and dimensionality reduction is discussed briefly. This linear transformation is useful in improving the approximation of probability distributions. Numerical examples are presented.

The objective of the present study has been to investigate possible methods to reduce a program's execution time by detecting and converting automatically the more frequently executed program parts, mostly inner loops, into microcode. The methods proposed were static loading of inner loops, selective loading of inner loops, over-lay of inner loops, and user-aided scheme. Using Fortran programs as the test programs, a simulation program was written to measure the gain achieved by each method. A final gain between 1.587 and 4.76 was achieved by the proposed methods for memory speed ratios between 3 and 8. It was found that 90% of the final gain of the test programs could be obtained with writable control memory requirements that were less than 40% of the final requirement.