IP-Enabled C/C++ Based High Level Synthesis: A Step towards Better Designer Productivity and Design Performance

Intellectual property (IP) core based design is an emerging design methodology to deal with increasing chip design complexity. C/C++ based high level synthesis (HLS) is also gaining traction as a design methodology to deal with increasing design complexity. In the work presented here, we present a design methodology that combines these two individual methodologies and is therefore more powerful. We discuss our proposed methodology in the context of supporting efficient hardware synthesis of a class of mathematical functions without altering original C/C++ source code. Additionally, we also discuss and propose methods to integrate legacy IP cores in existing HLS flows. Relying on concepts from the domains of program recognition and optimized low level implementations of such arithmetic functions, the described design methodology is a step towards intelligent synthesis where application characteristics are matched with specific architectural resources and relevant IP cores in a transparent manner for improved area-delay results. The combined methodology is more aware of the target hardware architecture than the conventional HLS flow. Implementation results of certain compute kernels from a commercial tool Vivado-HLS as well as proposed flow are also compared to show that proposed flow gives better results

Stochastic generation of annual, monthly and daily climate data: A review

International audienceThe generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed

Efficient reconfigurable techniques for VLSI arrays with 6-port switches

This paper proposes an efficient techniques to reconfigure a two-dimensional degradable very large scale integration/wafer scale integration (VLSI/WSI) array under the row and column routing constraints, which has been shown to be NP-complete. The proposed VLSI/WSI array consists of identical processing elements such as processors or memory cells embedded in a 6-port switch lattice in the form of a rectangular grid. It has been shown that the proposed VLSI structure with 6-port switches eliminates the need to incorporate internal bypass within processing elements and leads to notable increase in the harvest when compared with the one using 4-port switches. A new greedy rerouting algorithm and compensation approaches are also proposed to maximize harvest through reconfiguration. Experimental results show that the proposed VLSI array with 6-port switches consistently outperforms the most efficient alternative, proposed in literature, toward maximizing the harvest in the presence of fault processing elements

Augmented Lagrangian Methods as Layered Control Architectures

For optimal control problems that involve planning and following a trajectory, two degree of freedom (2DOF) controllers are a ubiquitously used control architecture that decomposes the problem into a trajectory generation layer and a feedback control layer. However, despite the broad use and practical success of this layered control architecture, it remains a design choice that must be imposed $a\ priori$ on the control policy. To address this gap, this paper seeks to initiate a principled study of the design of layered control architectures, with an initial focus on the 2DOF controller. We show that applying the Alternating Direction Method of Multipliers (ADMM) algorithm to solve a strategically rewritten optimal control problem results in solutions that are naturally layered, and composed of a trajectory generation layer and a feedback control layer. Furthermore, these layers are coupled via Lagrange multipliers that ensure dynamic feasibility of the planned trajectory. We instantiate this framework in the context of deterministic and stochastic linear optimal control problems, and show how our approach automatically yields a feedforward/feedback-based control policy that exactly solves the original problem. We then show that the simplicity of the resulting controller structure suggests natural heuristic algorithms for approximately solving nonlinear optimal control problems. We empirically demonstrate improved performance of these layered nonlinear optimal controllers as compared to iLQR, and highlight their flexibility by incorporating both convex and nonconvex constraints

Knapsack Model and Algorithm for Hardware/Software Partitioning Problem

Efficient hardware/software partitioning is crucial towards realizing optimal solutions for constraint driven embedded systems. The size of the total solution space is typically quite large for this problem. In this paper, we show that the knapsack model could be employed for the rapid identification of hardware components that provide for time efficient implementations. In particular, we propose a method to split the problem into standard 0-1 knapsack problems in order to leverage on the classical approaches. The proposed method relies on the tight lower and upper bounds for each of these knapsack problems for the rapid elimination of the sub-problems, which are guaranteed not to give optimal results. Experimental results show that, for problem sizes ranging from 30 to 3000, the optimal solution of the whole problem can be obtained by solving only 1 sub-problem except for one case where it required the solution of 3 sub-problems

A real time correlator architecture using distributed arithmetic principles

A real time correlator design based on the principles of Distributed Arithmetic (DA) is described. This design is shown to be more efficient in terms of memory requirement than the direct DA implementation, especially when the number of coefficients is large. Since the proposed architecture implements the sum of product evaluation, it can be easily extended to finite and infinite response filters. Methods to further reduce the memory requirements are also discussed. A brief comparison is made between the proposed method and different DA implementations

The Role of Na/K-ATPase Signaling in Oxidative Stress Related to Obesity and Cardiovascular Disease

Na/K-ATPase has been extensively studied for its ion pumping function, but, in the past several decades, has been identified as a scaffolding and signaling protein. Initially it was found that cardiotonic steroids (CTS) mediate signal transduction through the Na/K-ATPase and result in the generation of reactive oxygen species (ROS), which are also capable of initiating the signal cascade. However, in recent years, this Na/K-ATPase/ROS amplification loop has demonstrated significance in oxidative stress related disease states, including obesity, atherosclerosis, heart failure, uremic cardiomyopathy, and hypertension. The discovery of this novel oxidative stress signaling pathway, holds significant therapeuti

Concurrent Constrained Optimization of Unknown Rewards for Multi-Robot Task Allocation

Task allocation can enable effective coordination of multi-robot teams to accomplish tasks that are intractable for individual robots. However, existing approaches to task allocation often assume that task requirements or reward functions are known and explicitly specified by the user. In this work, we consider the challenge of forming effective coalitions for a given heterogeneous multi-robot team when task reward functions are unknown. To this end, we first formulate a new class of problems, dubbed COncurrent Constrained Online optimization of Allocation (COCOA). The COCOA problem requires online optimization of coalitions such that the unknown rewards of all the tasks are simultaneously maximized using a given multi-robot team with constrained resources. To address the COCOA problem, we introduce an online optimization algorithm, named Concurrent Multi-Task Adaptive Bandits (CMTAB), that leverages and builds upon continuum-armed bandit algorithms. Experiments involving detailed numerical simulations and a simulated emergency response task reveal that CMTAB can effectively trade-off exploration and exploitation to simultaneously and efficiently optimize the unknown task rewards while respecting the team's resource constraints.Comment: 9 pages, 5 figures, to be published in RSS 202

Comprehensive loss optimization of induction motor drives

Extensive use of power electronics-controlled induction motor drives over the past few decades has enabled the development of loss minimization control algorithms. With the technological advancements in power semiconductor switching devices such as insulated gate bipolar transistors and gate commutated thyristors, induction motor drives are increasingly used in applications, ranging from automotive traction to more-electric aircraft, which have widely varying speed, torque and power requirements. Advances in control technology have enabled the development of various sophisticated controllers for motor drives aimed at performance enhancement. Substantial energy savings may be obtained when drive controllers are optimized for loss reduction under varying operating conditions. This dissertation addresses loss optimization opportunities in induction motor drives from system perspectives. First, a constrained loss optimization method is developed. Past work on loss minimization has focused on specific drive components such as the machine stator and rotor windings, inverter and dc-link. Component-level loss minimization, however, will not guarantee minimum total loss in the drive system. So, a system-level loss minimization method is proposed using a comprehensive loss model, to achieve true minimum total loss. Next, a lossless damping controller is proposed to suppress undesirable resonant oscillations in the machine voltages and currents due to the use of LC filters between the inverter and motor terminals. Passive damping methods employing physical resistors to suppress these oscillations, contribute to additional losses. Lossless active damping methods with virtual resistors have been explored in the literature. Conventionally, this resistance value is fixed, based on empirical rules, and left unchanged for all operating conditions. Choosing incorrect resistance values for the damping controller can result in degraded system behavior. A small-signal transfer function approach based on operating conditions and dynamic adjustment of the virtual resistance, is developed for the damping controller. The controller is designed to allow a flexible differential damping approach. Finally, power electronics loss reduction is investigated in a voltage source inverter (VSI)-based induction motor drive. It is known that low drive speeds will result in poor bus utilization and increased power electronics loss for higher link voltages. Losses can be reduced by dynamically varying the dc link voltage according to operating conditions. In addition to reducing losses, varying the link voltage also reduces the switched voltage magnitude across the inverter switches, potentially increasing inverter reliability. In the proposed method, the link voltage is varied using a front-end dc-dc buck converter according to a loss minimization algorithm. The effect of additional loss from the front-end converter on the total loss is also studied. Benefits of the proposed methods are verified by simulations and experiments