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%

Comparison of the Worst and Best Sum-of-Products Expressions for Multiple-Valued Functions

Because most practical logic design algorithms produce irredundant sum-of-products (ISOP) expressions, the understanding of ISOPs is crucial. We show a class of functions for which Morreale-Minato's ISOP generation algorithm produces worst ISOPs (WSOP), ISOPs with the most product terms. We show this class has the property that the ratio of the number of products in the WSOP to the number in the minimum ISOP (MSOP) is arbitrarily large when the number of variables is unbounded. The ramifications of this are significant; care must be exercised in designing algorithms that produce ISOPs. We also show that 2/sup n-1/ is a firm upper bound on the number of product terms in any ISOP for switching functions on n variables, answering a question that has been open for 30 years. We show experimental data and extend our results to functions of multiple-valued variables

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed

Automated synthesis and optimization of multilevel logic circuits.

With the increased complexity of Very Large Scaled Integrated (VLSI) circuits, multilevellogic synthesis plays an even more important role due to its flexibility and compactness.The history of symbolic logic and some typical techniques for multilevel logic synthesisare reviewed. These methods include algorithmic approach; Rule-Based approach; BinaryDecision Diagram (BDD) approach; Field Programmable Gate Array(FPGA) approachand several perturbation applications.One new kind of don't cares (DCs), called functional DCs has been proposed for multilevellogic synthesis. The conventional two-level cubes are generalized to multilevel cubes.Then functional DCs are generated based on the properties of containment. The conceptof containment is more general than unateness which leads to the generation of newDCs. A separate C program has been developed to utilize the functional DCs generatedas a Boolean function is decomposed for both single output and multiple output functions.The program can produce better results than script.rugged of SIS, developed by UC Berkeley,both in area and speed in less CPU time for a number of testcases from MCNC andIWLS'93 benchmarks.In certain applications ANDjXOR (Reed-Muller) logic has shown some attractive advantagesover the standard Boolean logic based on AND JOR operations. A bidirectionalconversion algorithm between these two paradigms is presented based on the concept of polarityfor sum-of-products (SOP) Boolean functions, multiple segment and multiple pointerfacilities. Experimental results show that the algorithm is much faster than the previouslypublished programs for any fixed polarity. Based on this algorithm, a new technique calledredundancy-removal is applied to generalize the idea to very large multiple output Booleanfunctions. Results for benchmarks with up to 199 inputs and 99 outputs are presented.Applying the preceding conversion program, any Boolean functions can be expressedby fixed polarity Reed-Muller forms. There are 2n polarities for an n-variable function andthe number of product terms depends on these polarities. The problem of exact polarityminimization is computationally extensive and current programs are only suitable whenn :::; 15. Based on the comparison of the concepts of polarity in the standard Boolean logicand Reed-Muller logic, a fast algorithm is developed and implemented in C language whichcan find the best polarity for multiple output functions. Benchmark examples of up to 25inputs and 29 outputs run on a personal computer are given.After the best polarity for a Boolean function is calculated, this function can be furthersimplified using mixed polarity methods by combining the adjacent product terms. Hence,an efficient program is developed based on decomposition strategy to implement mixedpolarity minimization for both single output and very large multiple output Boolean functions.Experimental results show that the numbers of product terms are much less thanthe results produced by ESPRESSO for some categories of functions

Algebraic Characterization of CNOT-Based Quantum Circuits with its Applications on Logic Synthesis

The exponential speed up of quantum algorithms and the fundamental limits of current CMOS process for future design technology have directed attentions toward quantum circuits. In this paper, the matrix specification of a broad category of quantum circuits, i.e. CNOT-based circuits, are investigated. We prove that the matrix elements of CNOT-based circuits can only be zeros or ones. In addition, the columns or rows of such a matrix have exactly one element with the value of 1. Furthermore, we show that these specifications can be used to synthesize CNOT-based quantum circuits. In other words, a new scheme is introduced to convert the matrix representation into its SOP equivalent using a novel quantum-based Karnaugh map extension. We then apply a search-based method to transform the obtained SOP into a CNOT-based circuit. Experimental results prove the correctness of the proposed concept.Comment: 8 pages, 13 figures, 10Th EUROMICRO Conference on Digital System Design, Architectures, Methods and Tools, Germany, 200

Evolutionary Many-objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation

Many real-world optimization problems have more than three objectives, which has triggered increasing research interest in developing efficient and effective evolutionary algorithms for solving many-objective optimization problems. However, most many-objective evolutionary algorithms have only been evaluated on benchmark test functions and few applied to real-world optimization problems. To move a step forward, this paper presents a case study of solving a many-objective hybrid electric vehicle controller design problem using three state-of-the-art algorithms, namely, a decomposition based evolutionary algorithm (MOEA/D), a non-dominated sorting based genetic algorithm (NSGA-III), and a reference vector guided evolutionary algorithm (RVEA). We start with a typical setting aiming at approximating the Pareto front without introducing any user preferences. Based on the analyses of the approximated Pareto front, we introduce a preference articulation method and embed it in the three evolutionary algorithms for identifying solutions that the decision-maker prefers. Our experimental results demonstrate that by incorporating user preferences into many-objective evolutionary algorithms, we are not only able to gain deep insight into the trade-off relationships between the objectives, but also to achieve high-quality solutions reflecting the decision-maker’s preferences. In addition, our experimental results indicate that each of the three algorithms examined in this work has its unique advantages that can be exploited when applied to the optimization of real-world problems

An overview of soft open points in electricity distribution networks

Soft open points (SOPs) are power electronic devices that are usually placed at normally open points of electricity distribution networks to provide flexible power control to the networks. This paper gives a comprehensive overview of both academic research and industrial practice on SOPs in electricity distribution networks. The topologies of SOPs as multi-functional power electronic devices are identified and compared, which include back-to-back voltage source converters, multi-terminal voltage source converters, unified power flow controllers, and direct AC-to-AC modular multilevel converters. The academic research is reviewed in three aspects, i.e., benefit quantification, control, and optimal siting and sizing of SOPs. The benefit quantification indices are categorized into feeder load balancing, voltage profile improvement, power losses reduction, three-phase balancing and DG hosting capacity enhancement. The control of SOPs is summarized as a three-level control structure, where the system-level and converter-level control are further discussed. For optimal siting and sizing of SOPs, problem formulation and solution methods are analyzed. Besides the academic research, practical industrial projects of SOPs worldwide are also summarized. Finally, opportunities of research and industrial application of SOPs are discussed

Large Language Model for Multi-objective Evolutionary Optimization

Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully handcrafted design with domain knowledge. Recently, some attempts have been made to replace the manually designed operators in MOEAs with learning-based operators (e.g., neural network models). However, much effort is still required for designing and training such models, and the learned operators might not generalize well on new problems. To tackle the above challenges, this work investigates a novel approach that leverages the powerful large language model (LLM) to design MOEA operators. With proper prompt engineering, we successfully let a general LLM serve as a black-box search operator for decomposition-based MOEA (MOEA/D) in a zero-shot manner. In addition, by learning from the LLM behavior, we further design an explicit white-box operator with randomness and propose a new version of decomposition-based MOEA, termed MOEA/D-LO. Experimental studies on different test benchmarks show that our proposed method can achieve competitive performance with widely used MOEAs. It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings. The results reveal the potential benefits of using pre-trained LLMs in the design of MOEAs

Combinational logic synthesis based on the dual form of Reed-Muller representation

In certain applications, AND/XOR (Reed-Muller), and ORlXNOR (Dual form of Reed-Muller) logic have shown some attractive advantages over the standard Sum of Products (SOP) and Product of Sums (POS). Bidirectional conversion algorithms between SOP and AND/XOR also between POS and ORlXNOR based on Sparse and partitioning techniques are presented for multiple output Boolean functions. The developed programs are tested for some benchmarks with up to 20 inputs and 40 outputs. A new direct method is presented to calculate the coefficients of the Fixed Polarity Dual Reed-Muller (FPDRM) from the truth vector of the POS. Any Boolean function can be expressed by FPDRM forms. There are 211 polarities for an n-variable function and the number of sum terms depends on these polarities. Finding the best polarity is costly interims of CPU time, in order to search for the best polarity which will lead to the minimum number of sums for a particular function. Therefore, an algorithm is developed to compute all the coefficients of the Fixed Polarity Dual Reed-Muller (FPDRM) with polarity p from any polarity q. This technique is used to find the best polarity of FPDRM among the 211 fixed polarities. The algorithm is based on the Dual- polarity property and the Gray code strategy. Therefore, there is no need to start from POS form to find FPDRM coefficients for all the polarities. The proposed methods are efficient in terms of memory size and CPU time. A fast algorithm is developed and implemented in C language which can convert between POSs and FPDRMs. The program was tested for up to 23 variables. A modified version of the same program was used to find the best polarity. For up to 13 variables the CPU time was less than 42 seconds. To search for the optimal polarity for large number of variables and to reduce the se arch time 0 ffinding the 0 ptimal polarity 0 fthe function, two new algorithms are developed and presented in this thesis. The first one is used to convert between P OS and Positive Polarity Dual Reed-Muller (PPDRM) forms. The second algorithm will find the optimal fixed polarity for the FPDRM among the 211 different polarities for large n-variable functions. The most popular minimization criterion of the FPDRM form is obtained by the exhaustive search of the entire polarity vector. A non-exhaustive method for FPDRM expansions is presented. The new algorithms are based on separation of the truth vector (T) of POSs around each variable Xi into two groups. Instead of generating all of the polarity sets and searching for the best polarity, this algorithm will find the optimal polarity using the separation and sparse techniques, which will lead to optimal polarity. Time efficiency and computing speed are thus achieved in this technique. The algorithms don't require a large size of memory and don't require a long CPU time. The two algorithms are implemented in C language and tested for some benchmark. The proposed methods are fast and efficient as shown in the experimental results and can be used for large number of variables.EThOS - Electronic Theses Online ServiceGBUnited Kingdo