Communication-aware Heterogeneous Multiprocessor Mapping for Real-time Streaming Systems

Real-time streaming signal processing systems typically desire high throughput and low latency. Many such systems can be modeled as synchronous data flow graphs. In this paper, we address the problem of multi-objective mapping of SDF graphs onto heterogeneous multiprocessor platforms, where we account for the overhead of bus-based inter-processor communication. The primary contributions include (1) an integer linear programming (ILP) model that globally optimizes throughput, latency and cost; (2) low-complexity two-stage heuristics based on a combination of an evolutionary algorithm with an ILP to generate either a single sub-optimal mapping solution or a Pareto front for design space optimization. In our simulations, the proposed heuristic shows up to 12x run-time efficiency compared to the global ILP while maintaining a 10^− 6 optimality gap in throughput.     

A Tabu search algorithm for static routing and wavelength assignment problem

Static routing and wavelength assignment (RWA) is usually formulated as an optimization problem with the objective of minimizing wavelength usage (MWU). Existing solution methodologies for the MWU problem are usually based on a two-step approach, where routing and wavelength assignment are done independently. Though this approach can reduce computational cost, the optimality of the solution is compromised. We propose a novel tabu search (TS) algorithm, which considers routing and wavelength assignment jointly without increasing the computational complexity. The performance of the proposed TS algorithm is compared with the integer linear programming (ILP) method, which is known to solve the MWU to optimality. The results for both small and large networks show that our proposed TS algorithm works almost as well as the ILP solution and is much more computationally efficient.     

Synthesis for Mixed Arithmetic

The primary goal of this paper is to show that a clever use of redundant number systems in some parts of designs can significantly increase their speed, without noticeably increasing their area and power consumption. This can be achieved by automatically using, in the same design, redundant (e.g., carry save or borrow save) as well as non-redundant (i.e., conventional) number systems: this approach can be called mixed arithmetic. This implies specific constraints in the scheduling process. We propose an integer linear programming (ILP) formulation. It finds an optimal solution for examples of reasonable sizes. In some cases, the ILP computational delay may become huge. To solve this problem, we introduce a general solution, based on a constraint graph partitioning. This leads to an ILP formulation partitioning. This partitioning approach can be used for other similar problems in synthesis, also formulated as ILPs.     

Constrained shortest link-disjoint paths selection: a network programming based approach

Given a graph G with each link in the graph associated with two positive weights, cost and delay, we consider the problem of selecting a set of k link-disjoint paths from a node s to another node t such that the total cost of these paths is minimum and that the total delay of these paths is not greater than a specified bound. This problem, to be called the constrained shortest link-disjoint path (CSDP(k)) problem, can be formulated as an integer linear programming (ILP) problem. Relaxing the integrality constraints results in an upper bounded linear programming problem. We first show that the integer relaxations of the CSDP(k) problem and a generalized version of this problem to be called the generalized CSDP (GCSDP (k)) problem (in which each path is required to satisfy a specified bound on its delay) both have the same optimal objective value. In view of this, we focus our work on the relaxed form of the CSDP(k) problem (RELAX-CSDP(k)). We study RELAX-CSDP(k) from the primal perspective using the revised simplex method of linear programming. We discuss different issues such as formulas to identify entering and leaving variables, anti-cycling strategy, computational time complexity etc., related to an efficient implementation of our approach. We show how to extract from an optimal solution to RELAX-CSDP(k) a set of k link-disjoint s-t paths which is an approximate solution to the original CSDP(k) problem. We also derive bounds on the quality of this solution with respect to the optimum. We present simulation results that demonstrate that our algorithm is faster than currently available approaches. Our simulation results also indicate that in most cases the individual delays of the paths produced starting from RELAX-CSDP(k) do not deviate in a significant way from the individual path delay requirements of the GCSDP(k) problem.     

Energy-Efficient Information-Driven Coverage for Physical Movement Monitoring in Body Sensor Networks

Advances in technology have led to the development of various light-weight sensor devices that can be woven into the physical environment of our daily lives. Such systems enable on-body and mobile health-care monitoring. Our interest particularly lies in the area of movement-monitoring platforms that operate with inertial sensors. In this paper, we introduce the notion of compatibility graphs and describe how they can be utilized for power optimization. We first formulate an action coverage problem that will consider the sensing coverage from a collaborative signal processing perspective. Our solution is capable of eliminating redundant sensor nodes while maintaining the quality of service. The problem we outline can be transformed into an NP-hard problem. Therefore, we propose an ILP formulation to attain a lower bound on the solution and a fast greedy technique. Moreover, we present a system for dynamically activating and deactivating sensor nodes in real time. We then use our graph representation to develop an efficient formulation for maximum lifetime. This formulation provides sufficient information for finding activation duties for each sensor node. Finally, we demonstrate the effectiveness of our techniques on data collected from several subjects.     

基于粒子群优化的虚拟网络映射算法

本文以提高底层网络资源利用效率为目标,在底层网络不需要支持路径分裂的情况下,建立了虚拟网络映射问题的整数线性规划模型,并提出了一种新的基于粒子群优化的虚拟网络映射算法.该算法以映射开销作为适应度函数,重新对粒子的参数和相关操作进行了定义.模拟实验结果表明,与已有研究成果相比,该算法显著地提高了底层网络长期平均运营收益与...     

Cost Effective Shared Path Protection for WDM Optical Mesh Networks with Partial Wavelength Conversion

In this paper, we study routing and wavelength assignment of connection requests in survivable WDM optical mesh networks employing shared path protection with partial wavelength conversion while 100% restorability is guaranteed against any single failures. We formulate the problem as a linear integer program under a static traffic model. The objective is to minimize the total cost of wavelength-links and wavelength converters used by working paths and protection paths of all connections. A weight factor is used which is defined as the cost ratio of a wavelength converter and a wavelength-link. Depending on the relative cost of bandwidth and wavelength conversion, the optimization objective allows a proper tradeoff between the two. The proposed algorithm, the shortest-widest-path-first (SWPF) algorithm, uses a modified Dijkstra's algorithm to find a working path and a protection path for each connection request in the wavelength graph transformed from the original network topology. When there are multiple candidate paths that have the same minimum total cost, the path along which the maximum number of converters used at each node is minimized is chosen by the SWPF algorithm. We have evaluated the effectiveness of the proposed algorithm via extensive simulation. The results indicate that the performance of the proposed algorithm is very close to that of the optimal solutions obtained by solving the ILP formulation and outperforms existing heuristic algorithms in terms of total number of converters used and the maximum number of converters required at each node in the network. The proposed algorithm also achieves slightly better performance in terms of total cost of wavelength-links and converters used by all connections. We also investigated shared path protection employing converter sharing. The results show that the technique can reduce not only the total number of converters used in the network but also the maximum number of converters required at each node, especially when a large number of converters are needed in the network. In this study, although the ILP formulation is based on static traffic, the proposed algorithm is also applicable to routing dynamic connection requests.     

可生存的虚拟网络多层映射方法研究

本文研究了可生存虚拟网络多层映射问题,首先对其建立了整数线性规划模型（ILP）,然后针对较大规模问题提出一种高效的启发式算法VNP-SVNME对其进行求解.实验表明,VNP-SVNME算法的资源映射开销相对ILP仅平均高15%,且优于现有的启发式可生存算法.此外,VNP-SVNME算法的映射时间相对ILP大大降低,可以满足在线虚拟网络映射的需求.     

High-level synthesis for low power based on network flow method

We propose an effective algorithm for power optimization in behavioral synthesis. In previous work, it has been shown that several hardware allocation/binding problems for power optimization can be formulated as network flow problems and cand be solved optimally. However, in these formulations, a fixed schedule was assumed. In such a context, one key problem is that given an optimal network flow solution to a hardware allocation/binding problem for a given schedule, how to generate a new optimal network-flow solution rapidly for a local change of the given schedule. To this end, from a comprehensive analysis of the relation between network structure and flow computation, we devise a two-step procedure: Step 1) a max-flow computation step which finds a valid (maximum) flow solution while retaining the previous (maximum flow of minimum cost) solution as much as possible and Step 2) a min-cost computation step which incrementally refines the flow solution obtained in Step 1, using the concept of finding a negative cost cycle in the residual graph for the flow. The proposed algorithm can be applied effectively to several important high-level optimization problems (e.g., allocations/bindings of functional units, registers, buses, and memory ports) when we have the freedom to choose a schedule that will minimize power consumption. Experimental results (for bus synthesis) on benchmark problems show that our designs are 4%-40% more power-efficient over the designs produced by a random-move based solution and a clock-step based optimal solution, which is due to a) exploitation of the effect of scheduling and b) optimal binding for every schedule instance. Furthermore, our algorithm is about 2.6 times faster in run time over the full network flow based (optimal) algorithm, which is due to c) our novel (two-step) mechanism which utilizes the previous flow solution to reduce redundant flow computations.     

Dynamic Multicast Session Provisioning in WDM Optical Networks with Sparse Splitting Capability

In this work, we study dynamic provisioning of multicast sessions in a wavelength-routed sparse splitting capable WDM network with an arbitrary mesh topology where the network consists of nodes with full, partial, or no wavelength conversion capabilities and a node can be a tap-and-continue (TaC) node or a splitting and delivery (SaD) node. The objectives are to minimize the network resources in terms of wavelength-links used by each session and to reduce the multicast session blocking probability. The problem is to route the multicast session from each source to the members of every multicast session, and to assign an appropriate wavelength to each link used by the session. We propose an efficient online algorithm for dynamic multicast session provisioning. To evaluate the proposed algorithm, we apply the integer linear programming (ILP) optimization tool on a per multicast session basis to solve off-line the optimal routing and wavelength assignment given a multicast session and the current network topology as well as its residual network resource information. We formulate the per session multicast routing and wavelength assignment problem as an ILP. With this ILP formulation, the multicast session blocking probability or success probability can then be estimated based on solving a series of ILPs off-line. We have evaluated the effectiveness of the proposed online algorithm via simulation in terms of session blocking probability and network resources used by a session. Simulation results indicate that our proposed computationally efficient online algorithm performs well even when a fraction of the nodes are SaD nodes.     

利用冲突信息降维的进化高维目标优化算法

进化多目标优化算法求解高维目标优化问题面临收敛能力、计算复杂度、决策以及Pareto前沿的可视化等困难，其根本原因是目标空间维数高。目标降维通过丢弃冗余目标，为缓解高维目标优化求解困难提供一种新思路。本文提出利用冲突信息降维的分解进化高维目标优化算法（CIOR-MOEA/D）。该方法通过衡量目标在近似解集上体现的冲突性，构造问题的冲突信息矩阵，对该矩阵进行特征分析，确定目标的重要性程度，实现维数约简，并利用分解进化多目标优化算法（MOEA/D）对重要子目标集合进行分解进化,从而得到问题的近似解集。实验结果表明,本文提出的目标降维算法在降维的准确性与鲁棒性上均表现突出，能够有效地处理冗余高维目标优化问题。      

Telecommunication access network design with reliability constraints

In this paper, we study the problem of the design of telecommunication access networks with reliability constraints. These networks form an important part of the telecommunications infrastructure of large organizations, such as banks. Using data patterned after an actual bank network in the U.S., we formulate an optimization model for this problem which specifically takes into account the various cost, and discount structures offered by telecommunication carriers. We then develop dedicated solution procedures for obtaining solutions. Starting from a cluster solution, we then use perturbation techniques which we developed specifically for this problem within an overall simulated annealing solution algorithm. We show how to make the solution procedure more efficient by implicitly determining the values for many variables. We then report the results of our computational testing for a variety of problems. We compare our solution to a lower bound obtained using a linear programming relaxation. We show that substantial cost savings can be realized with our model, and solution procedure. Finally, we discuss which types of annealing steps in the simulated annealing algorithm are important.     

基于视觉显著图在线更新的结构反稀疏目标跟踪算法

为提高稀疏跟踪器性能,提出一种在贝叶斯推论框架下的基于视觉显著图的结构反稀疏在线目标跟踪算法。首先将基于马尔可夫(Markov)模型的关联性视觉显著度检测算法用于当前帧并计算目标模板的显著图,其次提出全局与局部分块的结构外观模型表示候选目标,将显著图映射回每一个局部块并计算出对应的自适应权重,最后提出联合全局与局部稀疏解的度量准则度量候选目标与目标模板的相似度,从而确立在贝叶斯框架下对目标状态最佳估计。在跟踪过程中,采用反稀疏表达方式一次求解优化问题计算出所有粒子权重来提高算法效率。实验结果表明,本文算法具有良好的鲁棒性和实时性。      

An architectural co-synthesis algorithm for distributed, embeddedcomputing systems

Many embedded computers are distributed systems, composed of several heterogeneous processors and communication links of varying speeds and topologies. This paper describes a new, heuristic algorithm which simultaneously synthesizes the hardware and software architectures of a distributed system to meet a performance goal and minimize cost. The hardware architecture of the synthesized system consists of a network of processors of multiple types and arbitrary communication topology; the software architecture consists of an allocation of processes to processors and a schedule for the processes. Most previous work in co-synthesis targets an architectural template, whereas this algorithm can synthesize a distributed system of arbitrary topology. The algorithm works from a technology database which describes the available processors, communication links, I/O devices, and implementations of processes on processors. Previous work had proposed solving this problem by integer linear programming (ILP); our algorithm is much faster than ILP and produces high-quality results     

A Graph Route-Based Superframe Scheduling Scheme in WirelessHART Mesh Networks for High Robustness

WirelessHART is considered to be one of the most promising wireless network protocols for its high robustness comparing to other similar wireless networks. The high robustness comes from its unique routing protocol and redundant superframe scheduling scheme. This paper focuses on the time-slot scheduling and channel assignment of WirelessHART and a graph route-based superframe scheduling scheme is proposed. In order to improve the communication reliability, our scheme applies hop-level retransmission mechanism in a multi-hop and multi-channel circumstance. Moreover, time-slot conflict and channel interference are considered and an effective solution strategy is proposed. To meet the real-time communication requirements, time-slots are assigned in the order of actual communication sequence which can effectively reduce the retransmission delay. Further more, we propose the implementation algorithm of our scheme. The performance analysis shows that our scheduling scheme has a higher robustness than the traditional non-redundancy scheme.     

时延约束的冗余通孔插入方法

Redundant via （RV） insertion is a useful mechanism to enhance via reliability. However, when extra vias are inserted into the design, the circuit timing might be changed. Therefore, how to insert RV under the timing constraints is the main challenge. In this paper, we introduce a new model to compute the distance between a RV and the corresponding single via, put forward a new RV type, which is called the long length via （LLV）, and then present an improved RV insertion method considering the timing constraints. This computing model can certify that the timing, which is obtained aider inserting a RV, is not greater than the original timing. Meanwhile, the new RV type LLV can increase the possibility of RV insertion; this method provides a global perspective for the RV insertion. Considering the timing constraints, the total redundant via insertion rate is 85.38% in the MIS-based method, while our proposed method can obtain a high insertion rate 88.79% for the tested circuits.     

A nature-inspired firefly algorithm based approach for nanoscale leakage optimal RTL structure

Optimization of leakage power is essential for nanoscale CMOS (nano-CMOS) technology based integrated circuits for numerous reasons, including improving battery life of the system in which they are used as well as enhancing reliability. Leakage optimization at an early stage of the design cycle such as the register-transfer level (RTL) or architectural level provides more degrees of freedom to design engineers and ensures that the design is optimized at higher levels before proceeding to the next and more detailed phases of the design cycle. In this paper, an RTL optimization approach is presented that targets leakage-power optimization while performing simultaneous scheduling, allocation and binding. The optimization approach uses a nature-inspired firefly algorithm so that large digital integrated circuits can be effectively handled without convergence issues. The firefly algorithm optimizes the cost of leakage delay product (LDP) under various resource constraints. As a specific example, gate-oxide leakage is optimized using a 45 nm CMOS dual-oxide based pre-characterized datapath library. Experimental results over various architectural level benchmark integrated circuits show that average leakage optimization of 90% can be obtained. For a comparative perspective, an integer linear programming (ILP) based algorithm is also presented and it is observed that the firefly algorithm is as accurate as ILP while converging much faster. To the best of the authors׳ knowledge, this is the first ever paper that applies firefly based algorithms for RTL optimization.     

WDM Network Design by ILP Models Based on Flow Aggregation

Planning and optimization of WDM networks has raised much interest among the research community in the last years. Integer linear programming (ILP) is the most used exact method to perform this task and many studies have been published concerning this issue. Unfortunately, many works have shown that, even for small networks, the ILP formulations can easily overwhelm the capabilities of today state-of-the-art computing facilities. So in this paper we focus our attention on ILP model computational efficiency in order to provide a more effective tool in view of direct planning or other benchmarking applications. Our formulation exploits flow aggregation and consists in a new ILP formulation that allows us to reach optimal solutions with less computational effort compared to other ILP approaches. This formulation applies to multifiber mesh networks with or without wavelength conversion. After presenting the formulation we discuss the results obtained in the optimization of case-study networks.     

基于笛卡尔乘积图上Sobolev平滑的时变图信号分布式批量重构

针对大规模网络数据的重构问题,该文以图信号处理(GSP)理论为基础,提出一种基于笛卡尔乘积图上Sobolev平滑的分布式批量重构算法(DBR-SSC)。该算法首先按时间顺序将时变图信号划分为多个信号段,并利用笛卡尔积将每一段内各时刻的图建模为乘积图;然后利用笛卡尔乘积图上的Sobolev差分平滑,将每一段的信号重构问题归结为优化问题;最后设计具有高收敛速度的分布式算法求解该优化问题。采用两种现实世界的数据集进行仿真实验,实验结果表明所提算法重构误差低并具有高收敛速度。