Nicholas H. Zamora

Computation and communication refinement for multiprocessor SoC design: A system-level perspective

Continuous advancements in semiconductor technology enable the design of complex systems-on-chips (SoCs) composed of tens or hundreds of IP cores. At the same time, the applications that need to run on such platforms have become increasingly complex and have tight power and performance requirements. Achieving a satisfactory design quality under these circumstances is only possible when both computation and communication refinement are performed efficiently, in an automated and synergistic manner. Consequently, formal and disciplined system-level design methodologies are in great demand for future multiprocessor design. This article provides a broad overview of some fundamental research issues and state-of-the-art solutions concerning both computation and communication aspects of system-level design. The methodology we advocate consists of developing abstract application and platform models, followed by application mapping onto the target platform, and then optimizing the overall system via performance analysis. In addition, a communication refinement step is critical for optimizing the communication infrastructure in this multiprocessor setup. Finally, simulation and prototyping can be used for accurate performance evaluation purposes.

Coordinated Distributed Power Management with Video Sensor Networks: Analysis, Simulation, and Prototyping

In this paper, we explore various design issues for coordinated distributed power management (CDPM) policies in wireless video sensor networks (VSNs). These CDPM policies help to efficiently power manage such networks while benefiting from the advantages gained by using distributed techniques. The design issues we explore include power management under dynamic and adaptive timeout thresholds, two-hop broadcast information dissemination, hybrid CDPM, and remote wakeup. Our investigations use an advanced, event-triggered VSN simulator, as well as a set of VSN prototype nodes we built as a proof-of-concept. Our prototype network includes four digital signal processing (DSP)-based wireless video nodes which form a small multi-hop network. Last but not least, we propose a novel analytical approach to predict the CDPM policy performance, and show that this analytical method matches the measured power savings in the prototype.

Distributed power-management techniques for wireless network video systems

Wireless sensor networks operating on limited energy resources need to be power efficient to extend the system lifetime. This is especially challenging for video sensor networks due to the large volumes of data they need to process in short periods of time. Towards this end, this paper proposes two coordinated power management policies for video sensor networks. These policies are scalable as the system grows and flexible to video parameters and network characteristics. In addition to simulation results, the prototype demonstrates the feasibility of implementing these policies. Finally, the analytical framework we provide gives an upper bound for the achievable sleep fraction and insight into how adjusting select parameters will affect the performance of the power management policies

System-level performance/power analysis for platform-based design of multimedia applications

The objective of this article is to introduce the use of Stochastic Automata Networks (SANs) as an effective formalism for application-architecture modeling in system-level average-case analysis for platform-based design. By platform, we mean a family of heterogeneous architectures that satisfy a set of architectural constraints imposed to allow re-use of hardware and software components. More precisely, we show how SANs can be used early in the design cycle to identify the best performance/power trade-offs among several application-architecture combinations. Having this information available not only helps avoid lengthy simulations for predicting power and performance figures, but also enables efficient mapping of different applications onto a chosen platform. We illustrate the benefits of our methodology by using the “Picture-in-Picture” video decoder as a driver application.

Fault-Tolerant Techniques for Ambient Intelligent Distributed Systems

Ambient Intelligent Systems provide an unexplored hardware platformfor executing distributed applications under strict energy constraints.These systems must respond quickly to changes in userbehavior or environmental conditions and must provide high availabilityand fault-tolerance under given quality constraints. Thesesystems will necessitate fault-tolerance to be built into applications.One way to provide such fault-tolerance is to employ the use of redundancy.Hundreds of computational devices will be available indeeply networked ambient intelligent systems, providing opportunitiesto exploit node redundancy to increase application lifetime orimprove quality of results if it drops below a threshold. Pre-copyingwith remote execution is proposed as a novel, alternative techniqueof code migration to enhance system lifetime for ambient intelligentsystems. Self-management of the system is considered in two differentscenarios: applications that tolerate graceful quality degradationand applications with single-point failures. The proposed techniquecan be part of a design methodology for prolonging the lifetime ofa wide range of applications under various types of faults, despitescarce energy resources.

Enabling multimedia using resource-constrained video processing techniques: A node-centric perspective

Successful proliferation of multimedia-enabled devices and advances in very large-scale integration (VLSI) technology has spawned new research efforts in migrating video processing applications onto ever smaller and more inexpensive devices. This article focuses on the technical challenges associated with that migration. Due to limitations in size, battery lifetime, and, ultimately, cost, mapping complex video applications onto resource-constrained systems is a very challenging proposition. To this end, we first consider a technique, region-of-interest (ROI) processing, of defining a window within a video frame and only operating on the data inside that window, ignoring the rest of the frame. By using this lossy technique, the processing requirements can be reduced by roughly 80% while the error introduced in the quality of the results is roughly 10%. The other technique is adaptive data partitioning (ADP) combined with a content-based power management algorithm. By distributing video processing among multiple processors and shutting them down when they are not needed, the energy consumed per processor can be reduced by 60% without sacrificing the performance of the underlying video-based application. Taken together, these novel techniques enable ambient multimedia systems and maintain the needed overall efficiency in video processing.

Data partitioning techniques for pervasive multimedia platforms

In this paper, we propose a method for mapping multimedia applications on systems with very limited resources (i.e. memory, computing capability and battery lifetime) by combining adaptive data partitioning with content-based dynamic power management. The potential of the approach is illustrated through a case study of an object tracking application running on a resource constrained system which can be embedded in the environment (e.g. offices, home or conference rooms) to offer significantly more opportunities for ubiquitous information, seamless communication, enhanced security, etc. compared to todays portable or stationary devices. Besides power and performance trade-offs, we also explore the scaling effects on data partitioning and provide insights for possible optimization when designing such systems

Resource-aware video processing techniques for ambient multimedia systems

Ambient intelligence (AmI) is the inconspicuous presence of computing into every facet of our lives. AmI systems of the future will contain devices with highly limited resources in terms of processing power, memory, and battery lifetime. Contrary to this are the memory, power, and cycle-hungry video processing applications which are required to provide the level of utility demanded by users. The paper introduces the idea of processing a portion of a video frame with the intent of achieving high levels of video processing performance while reducing considerably the hardware requirements for that processing. The techniques we propose operate only on a portion of the video frame and optionally adjust that portions size dynamically to match the video content reasonably well. Our results show that this technique can save roughly 75% in both memory and processing cycle requirements and up to 87% in energy consumption, while inducing less than 10% error in XY processing of the video frame