Approximate logic circuits: Theory and applications

CMOS technology scaling, the process of shrinking transistor dimensions based on Moore's law, has been the thrust behind increasingly powerful integrated circuits for over half a century. As dimensions are scaled to few tens of nanometers, process and environmental variations can significantly alter transistor characteristics, thus degrading reliability and reducing performance gains in CMOS designs with technology scaling. Although design solutions proposed in recent years to improve reliability of CMOS designs are power-efficient, the performance penalty associated with these solutions further reduces performance gains with technology scaling, and hence these solutions are not well-suited for high-performance designs. This thesis proposes approximate logic circuits as a new logic synthesis paradigm for reliable, high-performance computing systems. Given a specification, an approximate logic circuit is functionally equivalent to the given specification for a "significant" portion of the input space, but has a smaller delay and power as compared to a circuit implementation of the original specification. This contributions of this thesis include (i) a general theory of approximation and efficient algorithms for automated synthesis of approximations for unrestricted random logic circuits, (ii) logic design solutions based on approximate circuits to improve reliability of designs with negligible performance penalty, and (iii) efficient decomposition algorithms based on approxiiii mate circuits to improve performance of designs during logic synthesis. This thesis concludes with other potential applications of approximate circuits and identifies. open problems in logic decomposition and approximate circuit synthesis

Logic Synthesis for Established and Emerging Computing

Logic synthesis is an enabling technology to realize integrated computing systems, and it entails solving computationally intractable problems through a plurality of heuristic techniques. A recent push toward further formalization of synthesis problems has shown to be very useful toward both attempting to solve some logic problems exactly--which is computationally possible for instances of limited size today--as well as creating new and more powerful heuristics based on problem decomposition. Moreover, technological advances including nanodevices, optical computing, and quantum and quantum cellular computing require new and specific synthesis flows to assess feasibility and scalability. This review highlights recent progress in logic synthesis and optimization, describing models, data structures, and algorithms, with specific emphasis on both design quality and emerging technologies. Example applications and results of novel techniques to established and emerging technologies are reported

Scaling Soil Organic Carbon Sequestration for Climate Change Mitigation

Moving towards net zero GHG emissions by 2050 is likely a pre-condition for avoiding global warming higher than 1.5˚C by the end of the century. The land-use and agriculture sector can provide close to one third of this global commitment while ensuring food security, farmer resilience, and sustainable development. Protecting soil organic carbon (SOC) and sequestering carbon in organic matter-depleted soils might cost-effectively provide close to 15% of this target and support another 15% from large-scale restoration and implementation of best agronomic practices. Major players across food systems have recognized SOC’s potential and are setting up SOC sequestration-based targets to reduce corporate GHG emissions. However, farmer incentives, consumer education for informed choices, and transparent, accurate, consistent, and comparable methods for measurement, reporting, and verification (MRV) of changes in SOC stocks are lagging behind and preventing large-scale SOC protection and sequestration from fully taking off. Improvements in SOC MRV could be achieved notably through deploying new technologies and enabling standardized protocols at low transaction costs. The development of cost-effective SOC MRV would therefore help to unlock carbon assets and implementation of best agronomic practices at scale. This is especially applicable to developing countries where most of the opportunities to implement improved practices are found. Broadly speaking, developing countries are characterized by limitations in data availability and a lack of technical capacity and infrastructure for implementing and running a robust SOC MRV. In this context, the private sector and international development organizations – such as multilateral development banks (MDBs) – can play a crucial role given their global reach and investment capacity. By reviewing existing SOC MRV protocols and lessons learned from carbon projects that view SOC as a climate benefit and testing them against other projects, this report provides strategic recommendations to the World Bank Group’s (WBG) Carbon Markets and Innovation team (CMI) and Agriculture and Food Global Practice (GP) division. The recommendations provide guidance for implementing cost-effective SOC MRV of the WBG’s agricultural investments while improving the standardization of processes for creating carbon assets – with the potential to scale across multilateral and international development agencies and governments

Expert knowledge elicitation in the firefighting domain and the implications for training novices

Background/Purpose: Experienced fireground commanders are often required to make important decisions in time-pressured and dynamic environments that are characterized by a wide range of task constraints. The nature of these environments is such that firefighters are sometimes faced with novel situations that seek to challenge their expertise and therefore necessitate making knowledge-based as opposed to rule-based decisions. The purpose of this study is to elicit the tacitly held knowledge which largely underpinned expert competence when managing non-routine fire incidents. Design/Methodology/Approach: The study utilized a formal knowledge elicitation tool known as the critical decision method (CDM). The CDM method was preferred to other cognitive task analysis (CTA) methods as it is specifically designed to probe the cognitive strategies of domain experts with reference to a single incident that was both challenging and memorable. Thirty experienced firefighters and one staff development officer were interviewed in-depth across different fire stations in the UK and Nigeria (UK=15, Nigeria=16). The interview transcripts were analyzed using the emergent themes analysis (ETA) approach. Findings: Findings from the study revealed 42 salient cues that were sought by experts at each decision point. A critical cue inventory (CCI) was developed and cues were categorized into five distinct types based on the type of information each cue generated to an incident commander. The study also developed a decision making model — information filtering and intuitive decision making model (IFID), which describes how the experienced firefighters were able to make difficult fireground decisions amidst multiple informational sources without having to deliberate on their courses of action. The study also compiled and indexed the elicited tacit knowledge into a competence assessment framework (CAF) with which the competence of future incident commanders could potentially be assessed. Practical Implications: Through the knowledge elicitation process, training needs were identified, and the practical implications for transferring the elicited experts’ knowledge to novice firefighters were also discussed. The four component instructional design model aided the conceptualization of the CDM outputs for training purposes. Originality/Value: Although it is widely believed that experts perform exceptionally well in their domains of practice, the difficulty still lies in finding how best to unmask expert (tacit) knowledge, particularly when it is intended for training purposes. Since tacit knowledge operates in the unconscious realm, articulating and describing it has been shown to be challenging even for experts themselves. This study is therefore timely since its outputs can facilitate the development of training curricula for novices, who then will not have to wait for real fires to occur before learning new skills. This statement holds true particularly in this era where the rate of real fires and therefore the opportunity to gain experience has been on a decline. The current study also presents and discusses insights based on the cultural differences that were observed between the UK and the Nigerian fire service

Design optimisation of complex space systems under epistemic uncertainty

This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model.This thesis presents an innovative methodology for System Design Optimisation (SDO) through the framework of Model-Based System Engineering (MBSE) that bridges system modelling, Constrained Global Optimisation (CGO), Uncertainty Quantification (UQ), System Dynamics (SD) and other mathematical tools for the design of Complex Engineered and Engineering Systems (CEdgSs) under epistemic uncertainty. The problem under analysis has analogies with what is nowadays studied as Generative Design under Uncertainty. The method is finally applied to the design of Space Systems which are Complex Engineered Systems (CEdSs) composed of multiple interconnected sub-systems. A critical aspect in the design of Space Systems is the uncertainty involved. Much of the uncertainty is epistemic and is here modelled with Dempster Shafer Theory (DST). Designing space systems is a complex task that involves the coordination of different disciplines and problems. The thesis then proposes a set of building blocks, that is a toolbox of methodologies for the solution of problems which are of interest also if considered independently. It proposes then a holistic framework that couples these building blocks to form a SDO procedure. With regard to the building blocks, the thesis includes a network-based modelling procedure for CEdSs and a generalisation for CEdgSs where the system and the whole design process are both taken into account. Then, it presents a constraint min-max solver as an algorithmic procedures for the solution of the general Optimisation Under Uncertainty (OUU) problem. An extension of the method for the Multi-Objective Problems (MOP) is also proposed in Appendix as a minor result. A side contribution for the optimisation part refers to the extension of the global optimiser Multi Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) with the introduction of constraint handling and multiple objective functions. The Constraint Multi-Objective Problem (CMOP) solver is however a preliminary result and it is reported in Appendix. Furthermore, the thesis proposes a decomposition methodology for the computational reduction of UQ with DST. As a partial contribution, a second approach based on a Binary Tree decomposition is also reported in Appendix. With regard to the holistic approach, instead, the thesis gives a new dentition and proposes a framework for system network robustness and for system network resilience. It finally presents the framework for the optimisation of the whole design process through the use of a multi-layer network model

Application of high reliability theory in the water utility sector

In the literature, a need was identified to consider the provision of drinking water to be a ‘high reliability’ societal service. This thesis reports on an investigation into the technical and organisational reliability of a defined section in the water utility sector and a Regional Water Utility. Here, the organisational reliability in operations and incident management, and, secondly, the management of technical reliability of water supply systems arising from risk-based asset management were the emphasis of this project. In order to substantiate this investigation, three main research components were designed and conducted: firstly, a characterisation of the nature of incidents and their impact on customers; secondly, an investigation into organisational capabilities to manage incidents and its role in maintaining a resilient water supply system that minimises the impact of incidents on customers, and thirdly, an investigation into riskbased asset management strategies that provide and maintain the technical reliability of the water supply system. In the latter perspective, the opportunity to learn from previous incidents to enhance asset risk assessments was investigated. In this study, it was found that many HRO principles are readily observable in the water utilities that participated in this research. Following the characterisation of incidents, it is demonstrated that the observation of HRO principles during incident management has a positive effect on the overall reduction of incident impacts on customers. Beyond the immediate effect of HRO principles in incident management, it could be demonstrated that ‘learning from failure’ provides a mechanism to understand and manage future risks. The concept of incident meta-analysis is introduced that compares series of past incidents with documented perceived, future risks. The statistical analysis of incident time series facilitated the monitoring of incident trends, the validation of the risk model used in the Regional Water Utility and the verification of risk data, in particular for the risk components ‘probability, cause, effect and impact’

A Fast Method to Derive Minimum SOPs for Decomposable Functions

This paper shows that divide-and-conquer derives a minimum sum-of-products expression (MSOP) of functions that have an AND bi-decomposition when at least one of the subfunctions is orthodox. This extends a previous result showing that divide-and-conquer derives the MSOP of the AND bidecomposition of two orthodox functions. We show that divideand -conquer does not always produce an MSOP when neither function is orthodox. However, our experimental results show that, in this case, it derives a near minimal SOP. At the same time, our approach significantly reduces the time needed to find an MSOP or near minimal SOP. Also, we extend our results to functions that have a tri-decomposition

Systematic innovation : a comprehensive model for business and management with treatment on a South African case

Abstract: This thesis addresses innovation of business and management with the purpose of advancing innovation in South Africa. A Design Science Research methodology is utilised to evaluate the current knowledge base of business and management innovation and construct a high level model for Management Innovation that pertains to all management areas of business including technology and innovation management. This thesis evaluates Learn-by-Experimentation (Trial and Error), Van Gundy’s Structured Creative Processes and Mann’s model constructed in practice. The Learn-by-Experimentation is a methodology only suited for physical innovation. The Structured Creative Processes are found to be of a generic nature which is not suitable for Innovation of Business and Management. Mann’s model is a projection of TRIZ onto business and management that addresses a subset of the business areas. The literature study in this thesis showed the identification of innovation opportunities was explicitly addressed by Van Gundy and implicitly treated by Mann. The “General Internet Access” for South Africans, as envisioned in the National Development Plan to stimulate economic growth, has been analysed for systematic innovation potential and did not render the desired outcome. The NDP will require further development to enable systematic innovation. In the course of this research a spiral innovation model for systematic business and management is developed through intensive literature analysis to cover the identified gaps. The model consists of the following steps: 1. Identification 2. Analysis and Definition 3. Select Approach 4. Create Potential Solutions 5. Verify and Validate Solutions 6. Implement the best Verified and Validated Solution with the idea to converge towards an Ideal Final Result. The results of this study is a contribution to the knowledge base of business and management innovation.D.Ing. (Engineering Management

Value network modeling : a quantitative method for comparing benefit across exploration architectures

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 167-170).In the design of complex systems serving a broad group of stakeholders, it can be difficult to prioritize objectives for the architecture. I postulate that it is possible to make architectural decisions based on consideration of stakeholder value delivery, in order to help prioritize objectives. I introduce the concept of value network models to map out the indirect benefit delivered to stakeholders. A numerical methodology for prioritizing paths through this network model is presented, with a view to discovering the most important organizational outputs. I show how value network models can be linked to architecture models to provide decision support to the architect. I present a case study to examine the connectivity and sensitivity of a test architecture to value delivery. I conclude that a limited subset of NASA's outputs will discriminate between architectures. In this manner, I show how value considerations can be used to structure the design space before critical technical decisions are made to narrow it. A number of organizational implications for value delivery are generated from this analysis. In particular, I show that benefit flows should be aligned to organizational processes and responsibilities, and that failure to map stakeholder input to architecture evaluation can weaken benefit.by Bruce G. Cameron.S.M