scholarly journals Synthesis of reversible circuits for large reversible functions

2010 ◽  
Vol 23 (3) ◽  
pp. 273-286 ◽  
Author(s):  
Nouraddin Alhagi ◽  
Maher Hawash ◽  
Marek Perkowski
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Large Scale ◽  
Optimal Solution ◽  
Truth Table ◽  
Synthesis Process ◽  
Quantum Cost ◽  
Reversible Circuits ◽  
Gate Count ◽  
Reversible Circuit

This paper presents a new algorithm MP (multiple pass) to synthesize large reversible binary circuits without ancilla bits. The well-known MMD algorithm for synthesis of reversible circuits requires to store a truth table (or a Reed-Muller - RM transform) as a 2n vector to represent a reversible function of n variables. This representation prohibits synthesis of large functions. However, in MP we do not store such an exponentially growing data structure. The values of minterms are calculated in MP dynamically, one-by-one, from a set of logic equations that specify the reversible circuit to be designed. This allows for synthesis of large scale reversible circuits (30-bits), which is not possible with any existing algorithm. In addition, our unique multi-pass approach where the circuit is synthesized with various, yet specific, minterm orders yields quasi-optimal solution. The algorithm returns a description of the quasi-optimal circuit with respect to gate count or to its 'quantum cost'. Although the synthesis process in MP is relatively slower, the solution is found in real-time for smaller circuits of 8 bits or less.

Reversible Circuits Synthesis from Functional Decision Diagrams by using Node Dependency Matrices

2019 ◽  
Vol 29 (05) ◽  
pp. 2050079
Author(s):  
Suzana Stojković ◽  
Radomir Stanković ◽  
Claudio Moraga ◽  
Milena Stanković
Keyword(s):  
Data Structure ◽  
Optimal Order ◽  
Decision Diagrams ◽  
Circuit Synthesis ◽  
Quantum Cost ◽  
Dependency Matrix ◽  
Bottom Level ◽  
Reversible Circuits ◽  
Reversible Circuit ◽  
Toffoli Gates

Decision diagrams are a data structure suitable for reversible circuit synthesis. Functional decision diagrams (FDDs) are particularly convenient in synthesis with Toffoli gates, since the functional expressions for decomposition rules used in them are similar to the functional expressions of Toffoli gates. The main drawback of reversible circuit synthesis based on decision diagrams is the usually large number of ancilla lines. This paper presents two methods for the reduction of the number of ancilla lines in reversible circuits derived from FDDs by selecting the order of implementation of nodes. In the first method, nodes are implemented by levels, starting from the bottom level to the top. The method uses appropriately defined level dependency matrices for choosing the optimal order of implementation of nodes at the same level. In this way, the optimization is performed level by level. The second method uses a diagram dependency matrix expressing mutual dependencies among all the nodes in the diagram. This method is computationally more demanding than the first method, but the reductions of both the number of lines and the Quantum cost of the circuits are larger.

scholarly journals Reversible Circuit Synthesis Time Reduction Based on Subtree-Circuit Mapping

2020 ◽  
Vol 10 (12) ◽  
pp. 4147
Author(s):  
Amjad Hawash ◽  
Ahmed Awad ◽  
Baker Abdalhaq
Keyword(s):  
Large Scale ◽  
Experimental Tests ◽  
Synthesis Process ◽  
Circuit Synthesis ◽  
Quantum Cost ◽  
Synthesis Time ◽  
Reversible Circuit ◽  
Mapping Process ◽  
Time Reduction ◽  
Hash Key

Several works have been conducted regarding the reduction of the energy consumption in electrical circuits. Reversible circuit synthesis is considered to be one of the major efforts at reducing the amount of power consumption. The field of reversible circuit synthesis uses a large number of proposed algorithms to minimize the overall cost of circuits synthesis (represented in the line number and quantum cost), with minimal concern paid for synthesis time. However, because of the iterative nature of the synthesis optimization algorithms, synthesis time cannot be neglected as a parameter which needs to be tackled, especially for large-scale circuits which need to be realized by cascades of reversible gates. Reducing the synthesis cost can be achieved by Binary Decision Diagrams (BDDs), which are considered to be a step forward in this field. Nevertheless, the mapping of each BDD node into a cascade of reversible gates during the synthesis process is time-consuming. In this work, we implement the idea of the subtree-based mapping of BDD nodes to reversible gates instead of the classical nodal-based algorithm to effectively reduce the entire reversible circuit synthesis time. Considering Depth-First Search (DFS), we convert an entire BDD subtree in one step into a cascade of reversible gates. A look-up table for all possible combinations of subtrees and their corresponding reversible gates has been constructed, in which a hash key is used to directly access subtrees during the mapping process. This table is constructed as a result of a comprehensive study of all possible BDD subtrees and considered as a reference during the conversion process. The conducted experimental tests show a significant synthesis time reduction (around 95% on average), preserving the correctness of the algorithm in generating a circuit realizing the required Boolean function.

An Efficient Design for Testability Approach of Reversible Logic Circuits

2020 ◽  
pp. 2150094
Author(s):  
Joyati Mondal ◽  
Arighna Deb ◽  
Debesh K. Das
Keyword(s):  
Fault Detection ◽  
Low Power Design ◽  
Design For Testability ◽  
Control Input ◽  
Quantum Cost ◽  
Efficient Design ◽  
Additional Line ◽  
Reversible Circuits ◽  
Reversible Circuit ◽  
Additional Input

Reversible circuits have been extensively investigated because of their applications in areas of quantum computing or low-power design. A reversible circuit is composed of only reversible gates and allow computations from primary inputs to primary outputs and vice-versa. In the last decades, synthesis of reversible circuits received significant interest. Additionally, testing of these kinds of circuits has been studied which included different fault models and test approaches dedicated for reversible circuits only. The analysis of testability issues in a reversible circuit commonly involves the detection of the missing gate faults that may occur during the physical realizations of the reversible gates. In this paper, we propose a design for testability (DFT) technique for reversible circuits in which the gates of a circuit are clustered into different sets and the gates from each cluster are then connected to an additional input line where, the additional line acts as an extra control input to the corresponding gate. Such arrangement makes it possible to achieve [Formula: see text] fault detection in any reversible circuit with a small increase in quantum cost. Experimental evaluations confirm that the proposed DFT technique incurs less quantum cost overhead with [Formula: see text] fault detection compared to existing DFT techniques for reversible circuits.

A Data Structure for real-time Aggregation Queries of Big Brain Networks

2018 ◽  
Author(s):  
Florian Ganglberger ◽  
Joanna Kaczanowska ◽  
Wulf Haubensak ◽  
Katja Bühler
Keyword(s):  
Gene Expression ◽  
Data Structure ◽  
Real Time ◽  
Large Scale ◽  
State Of The Art ◽  
Brain Networks ◽  
Spatial Context ◽  
Brain Areas ◽  
Public Data ◽  
Aggregation Queries

AbstractRecent advances in neuro-imaging allowed big brain-initiatives and consortia to create vast resources of brain data that can be mined by researchers for their individual projects. Exploring the relationship between genes, brain circuitry, and behavior is one of key elements of neuroscience research. This requires fusion of spatial connectivity data at varying scales, such as whole brain correlated gene expression, structural and functional connectivity. With ever-increasing resolution, those exceed the past state-of-the art in several orders of magnitude in size and complexity. Current analytical workflows in neuroscience involve time-consuming manual aggregation of the data and only sparsely incorporate spatial context to operate continuously on multiple scales. Incorporating techniques for handling big connectivity data is therefore a necessity.We propose a data structure to explore heterogeneous neurobiological connectivity data for integrated visual analytics workflows. Aggregation Queries, i.e. the aggregated connectivity from, to or between brain areas allow experts the comparison of multimodal networks residing at different scales, or levels of hierarchically organized anatomical atlases. Executed on-demand on volumetric gene expression and connectivity data, they enable an interactive dissection of networks, with billions of edges, in real-time, and based on their spatial context. The data structure is optimized to be accessed directly from the hard disk, since connectivity of large-scale networks typically exceed the memory size of current consumer level PCs. This allows experts to embed and explore their own experimental data in the framework of public data resources without large-scale infrastructure.Our novel data structure outperforms state-of-the-art graph engines in retrieving connectivity of local brain areas experimentally. We demonstrate the application of our approach for neuroscience by analyzing fear-related functional neuroanatomy in mice. Further, we show its versatility by comparing multimodal brain networks linked to autism. Importantly, we achieve cross-species congruence in retrieving human psychiatric traits networks, which facilitates selection of neural substrates to be further studied in mouse models.

scholarly journals Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ride-Sharing Platforms

2021 ◽  
Vol 51 (5) ◽  
pp. 373-390
Author(s):  
Hao Yi Ong ◽  
Daniel Freund ◽  
Davide Crapis
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Optimal Solution ◽  
Monetary Incentives ◽  
Convex Programs ◽  
Personal Power ◽  
Lack Of Information ◽  
Short Run ◽  
Main Components ◽  
Levels Of Service

Drivers on the Lyft ride-share platform do not always know where the areas of supply shortage are in real time. This lack of information hurts both riders trying to find a ride and drivers trying to determine how to maximize their earnings opportunities. Lyft’s Personal Power Zone (PPZ) product helps the company to maintain high levels of service on the platform by influencing the spatial distribution of drivers in real time via monetary incentives that encourage them to reposition their vehicles. The underlying system that powers the product has two main components: (1) a novel “escrow mechanism” that tracks available incentive budgets tied to locations within a city in real time, and (2) an algorithm that solves the stochastic driver-positioning problem to maximize short-run revenue from riders’ fares. The optimization problem is a multiagent dynamic program that is too complicated to solve optimally for our large-scale application. Our approach is to decompose it into two subproblems. The first determines the set of drivers to incentivize and where to incentivize them to position themselves. The second determines how to fund each incentive using the escrow budget. By formulating it as two convex programs, we are able to use commercial solvers that find the optimal solution in a matter of seconds. Rolled out to all 320 cities in which Lyft operates in a little more than a year, the system now generates millions of bonuses that incentivize hundreds of thousands of active drivers to optimally position themselves in anticipation of ride requests every week. Together, the PPZ product and its underlying algorithms represent a paradigm shift in how Lyft drivers drive and generate earnings on the platform. Its direct business impact has been a 0.5% increase in incremental bookings, amounting to tens of millions of dollars per year. In addition, the product has brought about significant improvements to the driver and rider experience on the platform. These include statistically significant reductions in pick-up times and ride cancellations. Finally, internal surveys reveal that the vast majority of drivers prefer PPZs over the legacy system.

scholarly journals A modular approach for testable conservative reversible multiplexer circuit for nano-electronic confine application

2019 ◽  
Vol 9 (4) ◽  
pp. 299-309 ◽  
Author(s):  
Nirupma Pathak ◽  
Santosh Kumar ◽  
Neeraj Kumar Misra ◽  
Bandan Kumar Bhoi
Keyword(s):  
Control Unit ◽  
System Level ◽  
Quantum Cost ◽  
Optimal Parameters ◽  
Gate Count ◽  
Reversible Circuit ◽  
Art Works ◽  
Computing Framework ◽  
Quantum Technology ◽  
State Of Art

Abstract Quantum technology has an attractive application nowadays for its minimizing the energy dissipation, which is a prominent part of any system-level design. In this article, the significant module of a multiplexer, an extended to n:1 is framed with prominent application in the control unit of the processor. The proposed multiplexer modules are framed by the algorithm, which is extended perspective based. Further, quantum cost and gate count are less to ensure the efficient quantum computing framed. In addition, the QCA computing framework is an attempt to synthesize the optimal primitives in conservative reversible multiplexer in nano-electronic confine application. The developed lemmas is framed to prove the optimal parameters in the reversible circuit. Compared with existing state-of-art-works, the proposed modular multiplexer, the gate count, quantum cost and unit delay are optimal.

Novel Synthesis Methodology for Fault Tolerant Reversible Circuits by Bounded Model Checking for Linear Temporal Logic

2015 ◽  
Vol 24 (06) ◽  
pp. 1550091 ◽  
Author(s):  
Ming-Cui Li ◽  
Ri-Gui Zhou
Keyword(s):  
Model Checking ◽  
Temporal Logic ◽  
Fault Tolerant ◽  
Linear Temporal Logic ◽  
Bounded Model Checking ◽  
Quantum Cost ◽  
Step Method ◽  
Synthesis Time ◽  
Reversible Circuits ◽  
Reversible Circuit

Reversible circuit is of interest due to the characteristics of low energy consumption. This paper proposes a new scheme for synthesizing fault tolerant reversible circuits. A two-step method is put forward to convert an irreversible function into a parity-preserving reversible circuit. By introducing model checking for linear temporal logic, we construct a finite state machine to synthesize small reversible gates from elementary 1-qubit and 2-qubit gates, which is more automatic than the methods proposed previously. Constrains are increased so as to reduce the synthesis time in the two step method. The parity-preserving gate constructed by the two-step method has characteristics of low quantum cost because the quantum representation obtained from the counterexample for a given function in each step has the minimum quantum cost. In order to further reduce the quantum cost and decrease the synthesis time, the semi parity-preserving gates are put forward for the first time. These gates are parity-preserving when the auxiliary input is set to 0 and opposite parity when 1. Maintaining good robustness of the system in performing specific function, semi parity-preserving gate is conducive to detecting the stuck-at fault and partial gate fault in reversible circuits. The innovation of this paper is introducing the formal method to synthesis small fault tolerant gate, so as to construct the circuit with robust (semi) parity-preserving gates.

scholarly journals Displacement and visualization of point symbols based on spatial distribution characteristics

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Haipeng Liu ◽  
Yi Long ◽  
Yi Zheng
Keyword(s):  
Spatial Distribution ◽  
Data Structure ◽  
Real Time ◽  
Large Scale ◽  
Density Difference ◽  
Distribution Characteristics ◽  
Displacement Operator ◽  
Specific Location ◽  
Loss Of Information ◽  
Spatial Distribution Characteristics

<p><strong>Abstract.</strong> In WEB2.0 environment, the number of map-based mashups which display user-led POI data keeps increasing. When the cartographic processing of these map mashups is lacking, the display of the POI data showed on the maps are quite unsatisfactory because of the overlapping of symbols.</p><p>At present, some widely used methods commonly use selection and simplification operations based on a quadtree data structure, which can get a good result in the small and medium scales in which users mainly focus on the distribution characteristics and the density difference of POI, but will lose a lot of information in the large scales in which users mainly focus on the specific location and detailed information of the data. For example, two hotels with the same size will retain only one symbol after using selection or simplification operation although in the large scale if they are adjacent to each other, which will bring trouble to users when using maps. Displacement is a suitable operation to deal with this situation, however, current displacement methods face the problems of symbol position drift and nevertheless the loss of information in high-density areas.</p><p>In order to address these problems, this paper proposes a real-time POI visualization algorithm combining the characteristics of traditional quadtree data structure and the advantages of an improved displacement operator.</p>

scholarly journals Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4320 ◽  
Author(s):  
Michael Short ◽  
Sergio Rodriguez ◽  
Richard Charlesworth ◽  
Tracey Crosbie ◽  
Nashwan Dawood
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Industry 4.0 ◽  
Energy Demand ◽  
Large Scale ◽  
Optimal Solution ◽  
Hvac Systems ◽  
Economic Incentives ◽  
Building Stock ◽  
The Real

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.

Effective Service Composition in Large Scale Service Market

2012 ◽  
Vol 9 (1) ◽  
pp. 74-94 ◽  
Author(s):  
Xianzhi Wang ◽  
Zhongjie Wang ◽  
Xiaofei Xu
Keyword(s):  
Real Time ◽  
Service Composition ◽  
Large Scale ◽  
Effective Means ◽  
Optimal Solution ◽  
Solution Space ◽  
Coarse Grained ◽  
Service Market ◽  
Time Service ◽  
Special Cases

The web has undergone a tremendous shift from information repository to the provisioning capacity of services. As an effective means of constructing coarse-grained solutions by dynamically aggregating a set of services to satisfy complex requirements, traditional service composition suffers from dramatic decrease on the efficiency of determining the optimal solution when large scale services are available in the Internet based service market. Most current approaches look for the optimal composition solution by real-time computation, and the composition efficiency greatly depends on the adopted algorithms. To eliminate such deficiency, this paper proposes a semi-empirical composition approach which incorporates the extraction of empirical evidence from historical experiences to provide guidance to solution space reduction to real-time service selection. Service communities and historical requirements are further organized into clusters based on similarity measurement, and then the probabilistic correspondences between the two types of clusters are identified by statistical analysis. For each new request, its hosting requirement cluster would be identified and corresponding service clusters would be determined by leveraging Bayesian inference. Concrete services would be selected from the reduced solution space to constitute the final composition. Timing strategies for re-clustering and consideration to special cases in clustering ensures continual adaption of the approach to changing environment. Instead of relying solely on pure real-time computation, the approach distinguishes from traditional methods by combining the two perspectives together.

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