A Library-Based Synthesis Methodology for Reversible Logic

In this paper, a library-based synthesis methodology for reversible circuits is proposed where a reversible specification is considered as a permutation comprising a set of cycles. To this end, a pre-synthesis optimization step is introduced to construct a reversible specification from an irreversible function. In addition, a cycle-based representation model is presented to be used as an intermediate format in the proposed synthesis methodology. The selected intermediate format serves as a focal point for all potential representation models. In order to synthesize a given function, a library containing seven building blocks is used where each building block is a cycle of length less than 6. To synthesize large cycles, we also propose a decomposition algorithm which produces all possible minimal and inequivalent factorizations for a given cycle of length greater than 5. All decompositions contain the maximum number of disjoint cycles. The generated decompositions are used in conjunction with a novel cycle assignment algorithm which is proposed based on the graph matching problem to select the best possible cycle pairs. Then, each pair is synthesized by using the available components of the library. The decomposition algorithm together with the cycle assignment method are considered as a binding method which selects a building block from the library for each cycle. Finally, a post-synthesis optimization step is introduced to optimize the synthesis results in terms of different costs.Comment: 24 pages, 8 figures, Microelectronics Journal, Elsevie

Overview of Langley activities in active controls research

The application of active controls technology to reduce aeroelastic response of aircraft structures offers a potential for significant payoffs in terms of aerodynamic efficiency and weight savings. The activities of the Langley Research Center (laRC) in advancing active controls technology. Activities are categorized into the development of appropriate analysis tools, control law synthesis methodology, and experimental investigations aimed at verifying both analysis and synthesis methodology

A research program in active control/aeroelasticity in the JIAFS at NASA Langley Research Center

A control law synthesis methodology for multifunctional active control system to satisfy root-mean-square load and response constraints as well as to meet stability robustness requirements at plant input and output was developed. Modern control theory, singular value analysis and optimization techniques were utilized. All stability and response derivative expressions were derived analytically for sensitivity study. The software is incorporated as an update to the AB/LAD general control design software package PADLOCS

SCANN: Synthesis of Compact and Accurate Neural Networks

Deep neural networks (DNNs) have become the driving force behind recent artificial intelligence (AI) research. An important problem with implementing a neural network is the design of its architecture. Typically, such an architecture is obtained manually by exploring its hyperparameter space and kept fixed during training. This approach is time-consuming and inefficient. Another issue is that modern neural networks often contain millions of parameters, whereas many applications and devices require small inference models. However, efforts to migrate DNNs to such devices typically entail a significant loss of classification accuracy. To address these challenges, we propose a two-step neural network synthesis methodology, called DR+SCANN, that combines two complementary approaches to design compact and accurate DNNs. At the core of our framework is the SCANN methodology that uses three basic architecture-changing operations, namely connection growth, neuron growth, and connection pruning, to synthesize feed-forward architectures with arbitrary structure. SCANN encapsulates three synthesis methodologies that apply a repeated grow-and-prune paradigm to three architectural starting points. DR+SCANN combines the SCANN methodology with dataset dimensionality reduction to alleviate the curse of dimensionality. We demonstrate the efficacy of SCANN and DR+SCANN on various image and non-image datasets. We evaluate SCANN on MNIST and ImageNet benchmarks. In addition, we also evaluate the efficacy of using dimensionality reduction alongside SCANN (DR+SCANN) on nine small to medium-size datasets. We also show that our synthesis methodology yields neural networks that are much better at navigating the accuracy vs. energy efficiency space. This would enable neural network-based inference even on Internet-of-Things sensors.Comment: 13 pages, 8 figure

Synthesizing robust mode shapes with μ and implicit model following

Control synthesis problems involving assignment of closed-loop model shapes using implicit model following (IMF) structure are considered in the context of H_2, H∞ , and μ-synthesis theories. An extension to the dynamic output feedback case is given for the quadratic or H_2 IMF problem. The IMF problem is embedded within the framework of μ control theory, and extensions for including uncertainty are discussed. A robust synthesis methodology is presented using μ theory. An application of the robust IMF synthesis methodology to modal shape assignment for the longitudinal axis of a helicopter is demonstrated

Dynamic and Leakage Power-Composition Profile Driven Co-Synthesis for Energy and Cost Reduction

Recent research has shown that combining dynamic voltage scaling (DVS) and adaptive body bias (ABB) techniques achieve the highest reduction in embedded systems energy dissipation [1]. In this paper we show that it is possible to produce comparable energy saving to that obtained using combined DVS and ABB techniques but with reduced hardware cost achieved by employing processing elements (PEs) with separate DVS or ABB capability. A co-synthesis methodology which is aware of tasks’ power-composition profile (the ratio of the dynamic power to the leakage power) is presented. The methodology selects voltage scaling capabilities (DVS, ABB, or combined DVS and ABB) for the PEs, maps, schedules, and voltage scales applications given as task graphs with timing constraints, aiming to dynamic and leakage energy reduction at low hardware cost. We conduct detailed experiments, including a real-life example, to demonstrate the effectiveness of our methodology. We demonstrate that it is possible to produce designs that contain PEs with only DVS or ABB technique but have energy dissipation that are only 4.4% higher when compared with the same designs that employ PEs with combined DVS and ABB capabilities

LOT: Logic Optimization with Testability - new transformations for logic synthesis

A new approach to optimize multilevel logic circuits is introduced. Given a multilevel circuit, the synthesis method optimizes its area while simultaneously enhancing its random pattern testability. The method is based on structural transformations at the gate level. New transformations involving EX-OR gates as well as Reed–Muller expansions have been introduced in the synthesis of multilevel circuits. This method is augmented with transformations that specifically enhance random-pattern testability while reducing the area. Testability enhancement is an integral part of our synthesis methodology. Experimental results show that the proposed methodology not only can achieve lower area than other similar tools, but that it achieves better testability compared to available testability enhancement tools such as tstfx. Specifically for ISCAS-85 benchmark circuits, it was observed that EX-OR gate-based transformations successfully contributed toward generating smaller circuits compared to other state-of-the-art logic optimization tools

Creativity Screening of Product Innovation Based on Meta-synthesis Methodology

科学技术的迅猛发展、网络化以及经济全球化时代的到来，使得产品创新成为企业在激烈的国际环境中竞争的重要手段。从企业生产经营的流程来看，相对于生产和营销阶段，新产品开发中从创意产生到具体的创新方案提出的产品创新阶段，还没有实现科学化、工程化的管理和信息系统的支持。因此，单独对此阶段进行研究十分必要。本文将产品创新分为了产品需求、产品创意、产品设计、产品论证和产品评估阶段。并提出在对各阶段的复杂性进行分析的基础上，利用综合集成方法理论，根据各阶段需求和目标的不同，有针对性地提出解决方法，构建产品创新的信息支持系统。而本文重点研究产品创新中的创意筛选问题。 在本文的产品创意筛选研究中，在对创意词汇表...With the tougher competition of markets and the rapid development of technology, product innovation becomes more and more essential to enterprises. The new product development process can be divided into three stages including product innovation, manufacture and marketing. There are many researches on manufacture and marketing and they achieved information systems such as ERP to support these stag...学位：管理学硕士院系专业：管理学院_管理科学与工程学号：1772013115108