This paper proposes a method for mapping a finite state machine (FSM) into a two-dimensional array of LUTs, which is a part of our plastic cell architecture (PCA). LSIs based on the PCA have already implemented as asynchronous devices. Functions that run on the LSIs must also be asynchronous. In order to make good use of the LSIs, a system that translates functions into circuit information for the PCA is needed. We introduce a prototype system that maps an asynchronous FSM onto the PCA. First, a basic mapping method is considered, and then we create three methods to minimize circuit size. Some benchmark suites are synthesized to estimate their efficiency. Experimental results show that all the methods can map an asynchronous FSM onto the PCA and that the three methods can effectively reduce circuit size.

This paper describes the hierarchical behavioral description language celled SFL and its processing system. This integrated CAD system called PARTHENON is used for designs of the leading ASICs in the NTT Systems Labs. This paper shows, therefore, the effectiveness of PARTHENON as a practical high-lelel synthesis system through real design experience. SFL was developed to aid in the design of the hardware functions and behaviors of ASICs composed solely of clocksynchronized circuits. The main features of SFL are as follows: (1) It is not mixed with connection description, but employs only behavioral description (like procedual description in program language), and it provides hierarchical expression of behavioral description. (2) It permits the description of parallel processing operations by adopting a new hardware task concept. And, (3) it is linked with the behavioral simulator, logic synthesizer, and other components of the processing system. After describing SFL in some detail, a brief explanation of its synthesizer and other processing components is provided, along with its application results in the real design of some leading ASICs at the NTT Systems Laboratories.

Simple disjunctive decomposition is a special case of logic function decompositions, where variables are divided into two disjoint sets and there is only one newly introduced variable. It offers an optimal structure for a single-output function. This paper presents two techniques that enable us to apply simple disjunctive decompositions with little overhead. Firstly, we propose a method to find symple disjunctive decomposition forms efficiently by limiting decomposition types to be found to two: a decomposition where the bound set is a set of symmetric variables and a decomposition where the output function is a 2-input function. Secondly, we propose an algorithm that constructs a new logic representation for a simple disjunctive decomposition just by assigning constant values to variables in the original representation. The algorithm enables us to apply the decomposition with keeping good structures of the original representation. We performed experiments for decomposing functions and confirmed the efficiency of our method. We also performed experiments for restructuring fanout free cones of multi-level logic circuits, and obtained better results than when not restructuring them.

This paper presents a new efficient method for finding an "optimal" bi-decomposition form of a logic function. A bi-decomposition form of a logic function is the form: f(X) = α(g1(X1), g2(X2)). We call a bi-decomposition form optimal when the total number of variables in X1 and X2 is the smallest among all bi-decomposition forms of f. This meaning of optimal is adequate especially for the synthesis of LUT (Look-Up Table) networks where the number of function inputs is important for the implementation. In our method, we consider only two bi-decomposition forms; (g1 g2) and (g1 g2). We can easily find all the other types of bi-decomposition forms from the above two decomposition forms. Our method efficiently finds one of the existing optimal bi-decomposition forms based on a branch-and-bound algorithm. Moreover, our method can also decompose incompletely specified functions. Experimental results show that we can construct better networks by using optimal bi-decompositions than by using conventional decompositions.

This paper presents a new method that efficiently generates all of the kernels of a sum-of-products expression. Its main feature is the memorization of the kernel generation process by using a graph structure and implicit cube set representations. We also show its applications for common logic extraction. Our extraction method produces smaller circuits through several extensions than the extraction method based on two-cube divisors known as best ever.

This paper presents a logic synthesis method for look-up table (LUT) based field programmable gate arrays (FPGAs). We determine functions to be mapped to LUTs by functional decomposition for each of single-output functions. To share LUTs among several functions, we use a new Boolean resubstitution technique. Resubstitution is used to determine whether an existing function is useful to realize another function; thus, we can share common functions among two or more functions. The Boolean resubstitution proposed in this paper is customized for an LUT network synthesis because it is based on support minimization for an incompletely specified function. Experimental results show that our synthesis method produces a small size circuit in a practical amount of time.

This paper describes the realization of a dynamically reconfigurable logic LSI based on a novel parallel computer architecture. The key point of the architecture is its dual-structured cell array which enables dynamic and autonomous reconfiguration of the logic circuits. The LSI was completed by successfully introducing two specific features: fully asynchronous logic circuits and a homogeneous structure, only LUTs are used.

This paper proposes a method for implementing a metric computation accelerator for fractal image compression using reconfigurable hardware. The most time-consuming part in the encoding of this compression is computation of metrics among image blocks. In our method, each processing element (PE) configured for an image block accelerates these computations by pipeline processing. Furthermore, by configuring the PE for a specific image block, we can reduce the number of adders, which are the main computing elements, by a half even in the worst case.

This paper presents a new framework for synthesizing look-up table (LUT) networks. Some of the existing LUT network synthesis methods are based on one or two functional (Boolean) decompositions. Our method also uses functional decompositions, but we try to use various decomposition methods, which include algebraic decompositions. Therefore, this method can be thought of as a general framework for synthesizing LUT networks by integrating various decomposition methods. We use a cost database file which is a unique characteristic in our method. We also present comparisons between our method and some well-known LUT network synthesis methods, and evaluate the final results after placement and routing. Although our method is rather heuristic in nature, the experimental results are encouraging.

In this paper, we propose a transformation technique for the multiplications of one variable with multiple constants, which are frequently seen in the various applications of signal processing, image processing, and so forth. The method is based on the exploration of common subexpressions among constants and reduces the number of shifts, additions, and subtractions to implement linear computations with hardware. Our method searches for regularity among elements of a linear transform using matrix decomposition and generates a reduced data-flow graph which preserves the full regularity. We show experimental results obtained using Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) and illustrate the effectiveness of the method.

In this paper, we propose an efficient solution for the Multiple Constant Multiplication (MCM) problem. The method uses hierarchical clustering to exploit common subexpressions among constants and reduces the number of shifts, additions, and subtractions. The algorithm defines appropriate weights, which indicate operation priority, and selects common subexpressions, resulting in a minimum number of local operations. It can also be extended to various high-level synthesis tasks such as arbitrary linear transforms. Experimental results for several error-correcting codes, digital filters and Discrete Cosine Transforms (DCTs) have shown the effectiveness of our method.

Design points and the results seen in the development of a dynamically reconfigurable logic LSI, PCA-2, are described. PCA-2 enables the realization of flexible parallel processing based on the autonomous reconfiguration of logic circuits. To realize this feature, we introduce an asynchronous circuit design and a homogeneous cell array structure. PCA-2 represents an advance on the earlier LSI, PCA-1. Cutting edge CMOS technology is used to realize the structural merits of PCA hardware. Compared to PCA-1, PCA-2 offers 16 times greater integration level for programmable logic. Due to miniaturization and design refinement, PCA-2 provides a 6-fold increase in the circuit frequency of the configuration controller and a 3-fold increase in the operating frequency of the programmable logic. The results gained confirm the effects of refinement and the suitability of our architecture for device miniaturization.