Paulo F. Flores

Exact and Approximate Algorithms for the Optimization of Area and Delay in Multiple Constant Multiplications

The main contribution of this paper is an exact common subexpression elimination algorithm for the optimum sharing of partial terms in multiple constant multiplications (MCMs). We model this problem as a Boolean network that covers all possible partial terms that may be used to generate the set of coefficients in the MCM instance. We cast this problem into a 0-1 integer linear programming (ILP) by requiring that the single output of this network is asserted while minimizing the number of gates representing operations in the MCM implementation that evaluate to one. A satisfiability (SAT)-based 0-1 ILP solver is used to obtain the exact solution. We argue that for many real problems, the size of the problem is within the capabilities of current SAT solvers. Because performance is often a primary design parameter, we describe how this algorithm can be modified to target the minimum area solution under a user-specified delay constraint. Additionally, we propose an approximate algorithm based on the exact approach with extremely competitive results. We have applied these algorithms on the design of digital filters and present a comprehensive set of results that evaluate ours and existing approximation schemes against exact solutions under different number representations and using different SAT solvers.

Search algorithms for the multiple constant multiplications problem: Exact and approximate

This article addresses the multiplication of one data sample with multiple constants using addition/subtraction and shift operations, i.e., the multiple constant multiplications (MCM) operation. In the last two decades, many efficient algorithms have been proposed to implement the MCM operation using the fewest number of addition and subtraction operations. However, due to the NP-hardness of the problem, almost all the existing algorithms have been heuristics. The main contribution of this article is the proposal of an exact depth-first search algorithm that, using lower and upper bound values of the search space for the MCM problem instance, finds the minimum solution consuming less computational resources than the previously proposed exact breadth-first search algorithm. We start by describing the exact breadth-first search algorithm that can be applied on real mid-size instances. We also present our recently proposed approximate algorithm that finds solutions close to the minimum and is able to compute better bounds for the MCM problem. The experimental results clearly indicate that the exact depth-first search algorithm can be efficiently applied to large size hard instances that the exact breadth-first search algorithm cannot handle and the heuristics can only find suboptimal solutions.

An exact algorithm for the maximal sharing of partial terms in multiple constant multiplications

In this paper, we propose an exact algorithm that maximizes the sharing of partial terms in multiple constant multiplication (MCM) operations. We model this problem as a Boolean network that covers all possible partial terms which may be used to generate the set of coefficients in the MCM instance. The PIs to this network are shifted versions of the MCM input. An AND gate represents an adder or a subtracter, i.e., an AND gate generates a new partial term. All partial terms that have the same numerical value are ORed together. There is a single output which is an /spl and/ over all the coefficients in the MCM. We cast this problem into a 0-1 integer linear programming (ILP) problem by requiring that the output is asserted while minimizing the total number of AND gates that evaluate to one. A SAT-based solver is used to obtain the exact solution. We argue that for many real problems the size of the problem is within the capabilities of current SAT solvers. We present results using binary, CSD and MSD representations. Two main conclusions can be drawn from the results. One is that, in many cases, existing heuristics perform well, computing the best solution, or one close to it. The other is that the flexibility of the MSD representation does not have a significant impact in the solution obtained.

Prime implicant computation using satisfiability algorithms

The computation of prime implicants has several and significant applications in different areas, including automated reasoning, non-monotonic reasoning, electronic design automation, among others. The authors describe a new model and algorithm for computing minimum-size prime implicants of propositional formulas. The proposed approach is based on creating an integer linear program (ILP) formulation for computing the minimum-size prime implicant, which simplifies existing formulations. In addition, they introduce two new algorithms for solving ILPs, both of which are built on top of an algorithm for propositional satisfiability (SAT). Given the organization of the proposed SAT algorithm, the resulting ILP procedures implement powerful search pruning techniques, including a non-chronological backtracking search strategy, clause recording procedures and identification of necessary assignments. Experimental results, obtained on several benchmark examples, indicate that the proposed model and algorithms are significantly more efficient than other existing solutions.

An Exact Breadth-First Search Algorithm for the Multiple Constant Multiplications Problem

This paper addresses the multiplication of one data sample with multiple constants using addition/subtraction and shift operations, i.e., the multiple constant multiplications (MCM) problem. The MCM problem finds itself and its variants in many applications, such as digital finite impulse response (FIR) filters, linear signal transforms, and computer arithmetic. Although many efficient algorithms have been proposed to implement the MCM using the fewest number of operations, due to the NP-hardness of the problem, they have been heuristics, i.e., they cannot guarantee the minimum solution. In this work, we propose an exact algorithm based on the breadth-first search that finds the minimum number of operations solution of mid-size MCM instances in a reasonable time. The proposed exact algorithm has been tested on a set of instances including FIR filter and randomly generated instances, and compared with the previously proposed efficient heuristics. It is observed from the experimental results that, even though the previously proposed heuristics obtain similar results with the minimum number of operations solutions, there are instances for which the exact algorithm finds better solutions than the prominent heuristics.

Optimization of area in digital FIR filters using gate-level metrics

In the paper, we propose a new metric for the minimization of area in the generic problem of multiple constant multiplications, and demonstrate its effectiveness for digital FIR filters. Previous methods use the number of required additions or subtractions as a cost function. We make the observation that not all of these operations have the same design cost. In the proposed algorithm, a minimum area solution is obtained by considering area estimates for each operation. To this end, we introduce accurate hardware models for addition and subtraction operations in terms of gate-level metrics, under both signed and unsigned representations. Our algorithm not only computes the best design solution among those that have the same number of operations, but is also able to find better area solutions using a non-minimum number of operations. The results obtained by the proposed exact algorithm are compared with the results of the exact algorithm designed for the minimum number of operations on FIR filters and it is shown that the area of the design can be reduced by up to 18%.

On applying set covering models to test set compaction

Test set compaction is fundamental problem in digital system testing. In recent years, many competitive solutions have been proposed, most of which based on heuristics approaches. This paper studies the application of set covering models to the compaction of test sets, which can be used with any heuristic test set compaction procedure. For this purpose, recent and highly effective set covering algorithms are used. Experimental evidence suggests that the size of computed test sets can often be reduced by using set covering models and algorithms. Moreover a noteworthy empirical conclusion is that it may be preferable not to use fault simulation when the final objective is test set compaction.

Design of Digit-Serial FIR Filters: Algorithms, Architectures, and a CAD Tool

In the last two decades, many efficient algorithms and architectures have been introduced for the design of low-complexity bit-parallel multiple constant multiplications (MCM) operation which dominates the complexity of many digital signal processing systems. On the other hand, little attention has been given to the digit-serial MCM design that offers alternative low-complexity MCM operations albeit at the cost of an increased delay. In this paper, we address the problem of optimizing the gate-level area in digit-serial MCM designs and introduce high-level synthesis algorithms, design architectures, and a computer-aided design tool. Experimental results show the efficiency of the proposed optimization algorithms and of the digit-serial MCM architectures in the design of digit-serial MCM operations and finite impulse response filters.

Optimization Algorithms for the Multiplierless Realization of Linear Transforms

This article addresses the problem of finding the fewest numbers of addition and subtraction operations in the multiplication of a constant matrix with an input vector---a fundamental operation in many linear digital signal processing transforms. We first introduce an exact common subexpression elimination (CSE) algorithm that formalizes the minimization of the number of operations as a 0-1 integer linear programming problem. Since there are still instances that the proposed exact algorithm cannot handle due to the NP-completeness of the problem, we also introduce a CSE heuristic algorithm that iteratively finds the most common 2-term subexpressions with the minimum conflicts among the expressions. Furthermore, since the main drawback of CSE algorithms is their dependency on a particular number representation, we propose a hybrid algorithm that initially finds promising realizations of linear transforms using a numerical difference method, and then applies the proposed CSE algorithm to utilize the common subexpressions iteratively. The experimental results on a comprehensive set of instances indicate that the proposed approximate algorithms find competitive results with those of the exact CSE algorithm and obtain better solutions than the prominent, previously proposed, heuristics. It is also observed that our solutions yield significant area reductions in the design of linear transforms after circuit synthesis, compared to direct realizations of linear transforms.

Outstanding Issues in Model Order Reduction

With roots dating back to many years ago and applications in a wide variety of areas, model order reduction has emerged in the last few decades as a crucial step in the simulation, control, and optimization of complex physical systems. Reducing the order or dimension of models of such systems, is paramount to enabling their simulation and verification. While much progress has been achieved in the last few years regarding the robustness, efficiency and applicability of these techniques, certain problems of relevance still pose difficulties or renewed challenges that are not satisfactorily solved with the existing approaches. Furthermore, new applications for which dimension reduction is crucial, are becoming increasingly relevant, raising new issues in the quest for increased performance.