Advances in Functional Decomposition: Theory and Applications

Functional decomposition aims at finding efficient representations for Boolean functions. It is used in many applications, including multi-level logic synthesis, formal verification, and testing. This dissertation presents novel heuristic algorithms for functional decomposition. These algorithms take advantage of suitable representations of the Boolean functions in order to be efficient. The first two algorithms compute simple-disjoint and disjoint-support decompositions. They are based on representing the target function by a Reduced Ordered Binary Decision Diagram (BDD). Unlike other BDD-based algorithms, the presented ones can deal with larger target functions and produce more decompositions without requiring expensive manipulations of the representation, particularly BDD reordering. The third algorithm also finds disjoint-support decompositions, but it is based on a technique which integrates circuit graph analysis and BDD-based decomposition. The combination of the two approaches results in an algorithm which is more robust than a purely BDD-based one, and that improves both the quality of the results and the running time. The fourth algorithm uses circuit graph analysis to obtain non-disjoint decompositions. We show that the problem of computing non-disjoint decompositions can be reduced to the problem of computing multiple-vertex dominators. We also prove that multiple-vertex dominators can be found in polynomial time. This result is important because there is no known polynomial time algorithm for computing all non-disjoint decompositions of a Boolean function. The fifth algorithm provides an efficient means to decompose a function at the circuit graph level, by using information derived from a BDD representation. This is done without the expensive circuit re-synthesis normally associated with BDD-based decomposition approaches. Finally we present two publications that resulted from the many detours we have taken along the winding path of our research

Volumetric Untrimming: Precise decomposition of trimmed trivariates into tensor products

3D objects, modeled using Computer Aided Geometric Design tools, are traditionally represented using a boundary representation (B-rep), and typically use spline functions to parameterize these boundary surfaces. However, recent development in physical analysis, in isogeometric analysis (IGA) in specific, necessitates a volumetric parametrization of the interior of the object. IGA is performed directly by integrating over the spline spaces of the volumetric spline representation of the object. Typically, tensor-product B-spline trivariates are used to parameterize the volumetric domain. A general 3D object, that can be modeled in contemporary B-rep CAD tools, is typically represented using trimmed B-spline surfaces. In order to capture the generality of the contemporary B-rep modeling space, while supporting IGA needs, Massarwi and Elber (2016) proposed the use of trimmed trivariates volumetric elements. However, the use of trimmed geometry makes the integration process more difficult since integration over trimmed B-spline basis functions is a highly challenging task. In this work, we propose an algorithm that precisely decomposes a trimmed B-spline trivariate into a set of (singular only on the boundary) tensor-product B-spline trivariates, that can be utilized to simplify the integration process in IGA. The trimmed B-spline trivariate is first subdivided into a set of trimmed B\'ezier trivariates, at all its internal knots. Then, each trimmed B\'ezier trivariate, is decomposed into a set of mutually exclusive tensor-product B-spline trivariates, that precisely cover the entire trimmed domain. This process, denoted untrimming, can be performed in either the Euclidean space or the parametric space of the trivariate. We present examples on complex trimmed trivariates' based geometry, and we demonstrate the effectiveness of the method by applying IGA over the (untrimmed) results.Comment: 18 pages, 32 figures. Contribution accepted in International Conference on Geometric Modeling and Processing (GMP 2019

Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization

Enhancing Logic Synthesis of Switching Lattices by Generalized Shannon Decomposition Methods

In this paper we propose a novel approach to the synthesis of minimal-sized lattices, based on the decomposition of logic functions. Since the decomposition allows to obtain circuits with a smaller area, our idea is to decompose the Boolean functions according to generalizations of the classical Shannon decomposition, then generate the lattices for each component function, and finally implement the original function by a single composed lattice obtained by glueing together appropriately the lattices of the component functions. In particular we study the two decomposition schemes defining the bounded-level logic networks called P-circuits and EXOR-Projected Sums of Products (EP-SOPs). Experimental results show that about 34% of our benchmarks achieve a smaller area when implemented using the P-circuit decomposition for switching lattices, with an average gain of at least 25%, and about 27% of our benchmarks achieve a smaller area when implemented using the EP-SOP decomposition, with an average gain of at least 22%

Highly Automated Formal Verification of Arithmetic Circuits

This dissertation investigates the problems of two distinctive formal verification techniques for verifying large scale multiplier circuits and proposes two approaches to overcome some of these problems. The first technique is equivalence checking based on recurrence relations, while the second one is the symbolic computation technique which is based on the theory of Gröbner bases. This investigation demonstrates that approaches based on symbolic computation have better scalability and more robustness than state-of-the-art equivalence checking techniques for verification of arithmetic circuits. According to this conclusion, the thesis leverages the symbolic computation technique to verify floating-point designs. It proposes a new algebraic equivalence checking, in contrast to classical combinational equivalence checking, the proposed technique is capable of checking the equivalence of two circuits which have different architectures of arithmetic units as well as control logic parts, e.g., floating-point multipliers

On The Design Of Low-Complexity High-Speed Arithmetic Circuits In Quantum-Dot Cellular Automata Nanotechnology

For the last four decades, the implementation of very large-scale integrated systems has largely based on complementary metal-oxide semiconductor (CMOS) technology. However, this technology has reached its physical limitations. Emerging nanoscale technologies such as quantum-dot cellular automata (QCA), single electron tunneling (SET), and tunneling phase logic (TPL) are major candidate for possible replacements of CMOS. These nanotechnologies use majority and/or minority logic and inverters as circuit primitives. In this dissertation, a comprehensive methodology for majority/minority logic networks synthesis is developed. This method is capable of processing any arbitrary multi-output Boolean function to nd its equivalent optimal majority logic network targeting to optimize either the number of gates or levels. The proposed method results in different primary equivalent majority expression networks. However, the most optimized network will be generated as a nal solution. The obtained results for 15 MCNC benchmark circuits show that when the number of majority gates is the rst optimization priority, there is an average reduction of 45.3% in the number of gates and 15.1% in the number of levels. They also show that when the rst priority is the number of levels, an average reduction of 23.5% in the number of levels and 43.1% in the number of gates is possible, compared to the majority AND/OR mapping method. These results are better compared to those obtained from the best existing methods. In this dissertation, our approach is to exploit QCA technology because of its capability to implement high-density, very high-speed switching and tremendously lowpower integrated systems and is more amenable to digital circuits design. In particular, we have developed algorithms for the QCA designs of various single- and multi-operation arithmetic arrays. Even though, majority/minority logic are the basic units in promising nanotechnologies, an XOR function can be constructed in QCA as a single device. The basic cells of the proposed arrays are developed based on the fundamental logic devices in QCA and a single-layer structure of the three-input XOR function. This process leads to QCA arithmetic circuits with better results in view of dierent aspects such as cell count, area, and latency, compared to their best counterparts. The proposed arrays can be formed in a pipeline manner to perform the arithmetic operations for any number of bits which could be quite valuable while considering the future design of large-scale QCA circuits

Scalable Design and Synthesis of Reversible Circuits

The expectations on circuits are rising with their number of applications, and technologies alternative to CMOS are becoming more important day by day. A promising alternative is reversible computation, a computing paradigm with applications in quantum computation, adiabatic circuits, program inversion, etc. An elaborated design flow is not available to reversible circuit design yet. In this work, two directions are considered: Exploiting the conventional design flow and developing a new flow according to the properties of reversible circuits. Which direction should be taken is not obvious, so we discuss the possible assets and drawbacks of taking either direction. We present ideas which can be exploited and outline open challenges which still have to be addressed. Preliminary results obtained by initial implementations illustrate the way to go. By this we present and discuss two promising and complementary directions for the scalable design and synthesis of reversible circuits