On the design and synthesis of differential clock distribution network

This research work focuses mainly on the design and synthesis of Differential Clock Distribution Networks (DCDNs). The Clock Distribution Network (CDN) plays an important role in synchronous systems. The network is spread all over the chip to synchronize its sub-systems. The system performance is influenced by the performance of its clock network. As technology advances and the complexity increases, a drastic growth in the chip complexity in the near future is expected. Thus designing a reliable CDN is becoming a must, and therefore all the design efforts should be utilized to efficiently design clock distribution networks. Of importance in chip design are low power and low noise concepts. Differential signaling scheme offers high noise immunity and since it is associated with signal amplitudes lower than the usual, it may contribute to reduce power consumption as well. Due to these potentials, the design and analysis of DCDN has been the focus of this research work. First, a line equivalent delay model based on the decoupling method is proposed to be able to route DCDNs with minimum skew. This part refers to the routing and synthesis of DCDNS. Later, new configurations for differential buffers based on body-biased transistors are proposed, which show better performance for future low voltage applications. Finally, a circuit and system design method that reduces the power consumption of DCDNs is proposed. This is accomplished in two steps: First circuit configurations that reduce the differential voltage swing giving less power consumption are introduced. Later, by reducing the supply voltage, a DCDN is designed which has the same power consumption as single-node CDNs, but has less skew variation in the presence of external noises such as power supply fluctuations

Multilayer Modeling and Design of Energy Managed Microsystems

Aggressive energy reduction is one of the key technological challenges that all segments of the semiconductor industry have encountered in the past few years. In addition, the notion of environmental awareness and designing “green” products is yet another major driver for ultra low energy design of electronic systems. Energy management is one of the unique solutions that can address the simultaneous requirements of high-performance, (ultra) low energy and greenness in many classes of computing systems; including high-performance, embedded and wireless. These considerations motivate the focus of this dissertation on the energy efficiency improvement of Energy Managed Microsystems (EMM or EM2). The aim is to maximize the energy efficiency and/or the operational lifetime of these systems. In this thesis we propose solutions that are applicable to many classes of computing systems including high-performance and mobile computing systems. These solutions contribute to make such technologies “greener”. The proposed solutions are multilayer, since they belong to, and may be applicable to, multiple design abstraction layers. The proposed solutions are orthogonal to each other, and if deployed simultaneously in a vertical system integration approach, when possible, the net benefit may be as large as the multiplication of the individual benefits. At high-level, this thesis initially focuses on the modeling and design of interconnections for EM2. For this purpose, a design flow has been proposed for interconnections in EM2. This flow allows designing interconnects with minimum energy requirements that meet all the considered performance objectives, in all specified system operating states. Later, models for energy performance estimation of EM2 are proposed. By energy performance, we refer to the improvements of energy savings of the computing platforms, obtained when some enhancements are applied to those platforms. These models are based on the components of the application profile. The adopted method is inspired by Amdahl’s law, which is driven by the fact that ‘energy’ is ‘additive’, as ‘time’ is ‘additive’. These models can be used for the design space exploration of EM2. The proposed models are high-level and therefore they are easy to use and show fair accuracy, 9.1% error on average, when compared to the results of the implemented benchmarks. Finally, models to estimate energy consumption of EM2 according to their “activity” are proposed. By “activity” we mean the rate at which EM2 perform a set of predefined application functions. Good estimations of energy requirements are very useful when designing and managing the EM2 activity, in order to extend their battery lifetime. The study of the proposed models on some Wireless Sensor Network (WSN) application benchmark confirms a fair accuracy for the energy estimation models, 3% error on average on the considered benchmarks

D-SVM over Networked Systems with Non-Ideal Linking Conditions

This paper considers distributed optimization algorithms, with application in binary classification via distributed support-vector-machines (D-SVM) over multi-agent networks subject to some link nonlinearities. The agents solve a consensus-constraint distributed optimization cooperatively via continuous-time dynamics, while the links are subject to strongly sign-preserving odd nonlinear conditions. Logarithmic quantization and clipping (saturation) are two examples of such nonlinearities. In contrast to existing literature that mostly considers ideal links and perfect information exchange over linear channels, we show how general sector-bounded models affect the convergence to the optimizer (i.e., the SVM classifier) over dynamic balanced directed networks. In general, any odd sector-bounded nonlinear mapping can be applied to our dynamics. The main challenge is to show that the proposed system dynamics always have one zero eigenvalue (associated with the consensus) and the other eigenvalues all have negative real parts. This is done by recalling arguments from matrix perturbation theory. Then, the solution is shown to converge to the agreement state under certain conditions. For example, the gradient tracking (GT) step size is tighter than the linear case by factors related to the upper/lower sector bounds. To the best of our knowledge, no existing work in distributed optimization and learning literature considers non-ideal link conditions

Infection Curve Flattening via Targeted Interventions and Self-Isolation

Understanding the impact of network clustering and small-world properties on epidemic spread can be crucial in developing effective strategies for managing and controlling infectious diseases. Particularly in this work, we study the impact of these network features on targeted intervention (e.g., self-isolation and quarantine). The targeted individuals for self-isolation are based on centrality measures and node influence metrics. Compared to our previous works on scale-free networks, small-world networks are considered in this paper. Small-world networks resemble real-world social and human networks. In this type of network, most nodes are not directly connected but can be reached through a few intermediaries (known as the small-worldness property). Real social networks, such as friendship networks, also exhibit this small-worldness property, where most people are connected through a relatively small number of intermediaries. We particularly study the epidemic curve flattening by centrality-based interventions/isolation over small-world networks. Our results show that high clustering while having low small-worldness (higher shortest path characteristics) implies flatter infection curves. In reality, a flatter infection curve implies that the number of new cases of a disease is spread out over a longer period of time, rather than a sharp and sudden increase in cases (a peak in epidemic). In turn, this reduces the strain on healthcare resources and helps to relieve the healthcare services

Discretized Distributed Optimization over Dynamic Digraphs

We consider a discrete-time model of continuous-time distributed optimization over dynamic directed-graphs (digraphs) with applications to distributed learning. Our optimization algorithm works over general strongly connected dynamic networks under switching topologies, e.g., in mobile multi-agent systems and volatile networks due to link failures. Compared to many existing lines of work, there is no need for bi-stochastic weight designs on the links. The existing literature mostly needs the link weights to be stochastic using specific weight-design algorithms needed both at the initialization and at all times when the topology of the network changes. This paper eliminates the need for such algorithms and paves the way for distributed optimization over time-varying digraphs. We derive the bound on the gradient-tracking step-size and discrete time-step for convergence and prove dynamic stability using arguments from consensus algorithms, matrix perturbation theory, and Lyapunov theory. This work, particularly, is an improvement over existing stochastic-weight undirected networks in case of link removal or packet drops. This is because the existing literature may need to rerun time-consuming and computationally complex algorithms for stochastic design, while the proposed strategy works as long as the underlying network is weight-symmetric and balanced. The proposed optimization framework finds applications to distributed classification and learning

On the Efficient Design & Synthesis of Differential Clock Distribution Networks

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