2364475
Combined functional partitioning and communication speed selection for networked voltage-scalable processors
This paper presents a new technique for global energy optimization through coordinated functional partitioning and speed selection for embedded processors interconnected by a high-speed serial bus. Many such serial interfaces are capable of operating at multiple speeds and can open up a new dimension of trade-offs to complement today's CPU-centric voltage scaling techniques for processors. We propose a multi-dimensional dynamic programming formulation for energy-optimal functional partitioning with CPU/communication speed selection for a class of data-regular applications under performance constraints. We demonstrate the effectiveness of our optimization techniques with an image processing application mapped onto a multi-processor architecture with a multi-speed Ethernet.
1100335
Power-aware scheduling under timing constraints for mission-critical embedded systems
Power-aware systems are those that must make the best use of available power. They subsume traditional low-power systems in that they must not only minimize power when the budget is low, but also deliver high performance when required. This paper presents a new scheduling technique for supporting the design and evaluation of a class of power-aware systems in mission-critical applications. It satisfies stringent min/max timing and max power constraints. It also makes the best effort to satisfy the min power constraint in an attempt to fully utilize free power or to control power jitter. Experimental results show that our scheduler can improve performance and reduce energy cost simultaneously compared to hand-crafted designs for previous missions. This tool forms the basis of the IMPACCT system-level framework that will enable designers to explore many power-performance trade-offs with confidence.
894781
A constraint-based application model and scheduling techniques for power-aware systems
New embedded systems must be power-aware, not just low-power. That is, they must track their power sources and the changing power and performance constraints imposed by the environment. Moreover, they must fully explore and integrate many novel power management techniques. Unfortunately, these techniques are often incompatible with each other due to overspecialized formulations or they fail to consider system-wide issues. This paper proposes a new graph-based model to integrate novel power management techniques and facilitate design-space exploration of power-aware embedded systems. It captures min/max timing and min/max power constraints on computation and non-computation tasks through a new constraint classification and enables derivation of flexible system-level schedules. We demonstrate the effectiveness of this model with a power-aware scheduler on real mission-critical applications. Experimental results show that our automated techniques can improve performance and reduce energy cost simultaneously. The application model and scheduling tool presented in this paper form the basis of the IMPACCT system-level framework that will enable designers to aggressively explore many power-performance trade-offs with confidence.
29539267
Energy Optimization of Distributed Embedded Processors by Combined Data Compression and Functional Partitioning
11417595
Distributed embedded systems for low power: a case study
Summary form only given. A multiple-processor system can potentially achieve higher energy savings than a single processor, because the reduced workload on each processor creates new opportunities for dynamic voltage scaling (DVS). However, as the cost of communication starts to match or surpass that of computation, many new challenges arise in making DVS effective in a distributed system under communication-intensive workload. We discuss implementation issues for supporting DVS on distributed embedded processors. We implemented and evaluated four distributed schemes: (1) DVS during I/O, (2) partitioning, (3) power-failure recovery, and (4) node rotation. We validated the results on a distributed embedded system with the Itsy pocket computers connected by serial links. Our experiments confirmed that a distributed system can create new DVS opportunities and achieve further energy savings. However, a surprising result was that aggregate energy savings do not translate directly into a longer battery life. In fact, the best partitioning scheme, which distributed the workload onto two nodes and enabled the most power-efficient CPU speeds at 30-50%, resulted in only 15% improvement in battery lifetime. Of the four techniques evaluated, node rotation showed the most measurable improvement to battery lifetime at 45% by balancing the discharge rates among the nodes.
1037858
Power-Aware Task Motion for Enhancing Dynamic Range of Embedded Systems with Renewable Energy Sources
In this paper we propose a novel scheduling framework for a dynamic real-time environment that experiences power consumption constraints. This framework is capable of dynamically adjusting the voltage/ speed of the system, such that no task in the system misses its deadline and the total energy savings of the system are maximized. 
 
Each task in the system consumes a certain amount of energy, which depends on a speed chosen for execution. The process of selecting speeds for execution while maximizing the energy savings of the system requires the exploration of a large number of combinations, which is too time consuming to be computed on-line. Thus, we propose an integrated heuristic methodology which executes an optimization procedure and an approximate greedy algorithm in a low computation time. This scheme allows the scheduler to handle power-aware real-time tasks with low cost while maximizing the use of the available resources and without jeopardizing the temporal constraints of the system. Simulation results show that our heuristic methodology achieves a performance with near-optimal results.
46541251
Trustworthy Distributed Algorithm Design to Defend Distance Cheating in Link-Weighted ALM
In link-weighted Application Layer Multicast (ALM), each selfish and strategic end-host receiver may cheat in distance measurement to obtain a better position in the ALM tree. Distance cheating of ALM receivers might lead the ALM tree to be sub-optimal and unstable. We proposed a VCG-based cheat-proof mechanism to defend this kind of cheating behavior in previous work. Based on the theoretical framework, a practical algorithm is designed in this paper, which runs distributedly on ALM receivers. The primary characteristic of the distributed algorithm lies in that it is trustworthy to enforce each selfish receiver to fulfill the algorithm truthfully, which is not considered in other similar studies. Simulations results further suggest that the algorithm is effective in defending distance cheating in link-weighted ALM.
13586161
Shape from regularities for interactive 3D reconstruction of piecewise planar objects from single images
3D object reconstruction from single 2D images has many applications in multimedia. This paper proposes an approach based on image regularities such as connectivity, parallelism, and orthogonality possessed by the objects with simple user interactions. It is assumed that the objects are piecewise planar. By representing the 3D objects as a shape vector consisting of the normals of the faces of the objects, we impose geometric constraints on this shape vector using the regularities of the objects. We derive a system of equations in terms of the shape vector and the focal length, which we can solve for the shape vector optimally. Experimental results on real images are shown to demonstrate the effectiveness of this method.
53245242
RoamHBA: maintaining group connectivity in sensor networks
8844798
Learning not to share
Strong reasons exist for executing a large-scale discrete-event simulation on a cluster of processor nodes (each of which may be a shared-memory multiprocessor or a uniprocessor). This is the architecture of the largest scale parallel machines, and so the largest simulation problems can only be solved this way. It is a common architecture even in less esoteric settings, and is suitable for memory-bound simulations. This paper describes our approach to porting the SSF simulation kernel to this architecture, using the Message Passing Interface (MPI) system. The notable feature of this transformation is to support an efficient two-level synchronization and communication scheme that addresses cost discrepancies between shared-memory and distributed memory. In the initial implementation, we use a globally synchronous approach between distributed-memory nodes, and an asynchronous shared-memory approach within a SMP cluster. The SSF API reflects inherently shared-memory assumptions; we report therefore on our approach for porting an SSF kernel to a cluster of SMP nodes. Experimental results on two architectures are described, for a model of TCP/IP traffic flows over a hierarchical network. The performance on a distributed network of commodity SMPs connected through ethernet is seen to frequently exceed performance on a Sun shared-memory multiprocessor.
6396851
Multiagent diffusion and distributed optimization
Distributed problem solving by a multiagent system represents a promising approach to solving complex computational problems. However, many multiagent systems require certain degree of planning, coordination and negotiation to achieve the given goal. This paper presents a multiagent framework for tackling global optimization tasks inspired by diffusion in nature. The framework is designed for situations where agent communication must be kept to a minimal. Hence, complicated coordination and negotiation is not possible. Distributed agents in this framework share the common goal of finding the global optimal solution. They cooperate to achieve this common goal by sharing and updating a common belief that captures their estimation of the whereabouts of the optimal solution. To facilitate this, agents are naturally organized in families with a parent and its offsprings as members. This paper also presents an algorithm called Evolutionary Diffusion Optimization, which is implemented base on the proposed agent framework. Experimental results on some benchmark problems are presented together with performance comparison with a simulated annealing algorithm.