System Energy Efficiency Lab
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Current Research:

Emerging Computing

Today’s computing systems are designed to deliver only exact solutions at high energy cost, while many of the algorithms that are run on data are at their heart statistical, and thus do not require exact answers. We are working on a framework to optimally and simultaneously trade-off accuracy and efficiency across software and hardware stacks of IoT applications. In addition, running machine learning algorithms on embedded devices is crucial as many applications require real-time response. However, hardware implementation and high computation energy cost are the main bottlenecks of machine learning algorithms in big data domain. We search for alternative architectures to address the computing cost and memory movement issues of traditional cores.

The Internet of Things, Smart Cities, and Wireless Healthcare

In an increasingly informed world, generating and processing information encompasses several computing domains, from datacenters to embedded systems. SEELab research in this area includes efficient distributed data collection and aggregation to processing and adapting to this data in smart cities, data centers, the distributed smart grid and wireless healthcare applications.

The Internet of Things with Applications to Smart Grid and Green Energy

The Internet of Things creates both new opportunities and challegens in several different domains. The abundace of data helps researchers to better understand their surroudings and create effective and automated actuation solutions. SEELab's research efforts on this topic target to solve several problems, including renewable energy integration in large scale systems, individual load energy reduction and automation, energy storage, context-aware energy management, better prediction mechanisms, user activity modeling and smart grid pricing and load integration. To solve these problems, we design and implement multiple tools that not only model and analyze smaller individual pieces but also create a comprehensive representation of this vast environment.

Wireless Healthcare

With the proliferation of personal mobile computing via mobile phones and the advent of cheap, small sensors, we propose that a new kind of "citizen infrastructure", can be made pervasive at low cost and high value. Though challenges abound in mobile power management, data security, privacy, inference with commodity sensors, and "polite" user notification, the overriding challenge lies in the integration of the parts into a seamless yet modular whole that can make the most of each piece of the solution at every point in time through dynamic adaptation. Using existing integration methodologies would cause components to hide essential information from each other, limiting optimization possibilities. Emphasizing seamlessness and information sharing, on the other hand, would result in a monolithic solution that could not be modularly configured, adapted, maintained, or upgraded.

IoT System Characterization and Management: from Data Centers to Smart Devices and Sensors

The Internet of Things is a growing network of heterogeneous devices, combining commercial, industrial, residential and cloud-fog computing domains. These devices range from low-power sensors with limited capabilities to multi-core platforms on the high-end. The IoT systems creates both new opportunities and challenges in several different domains. The abundance of data helps researchers to better understand their surroundings and create automated solutions which effectively model and manage diverse constrained resources in IoT devices and networks, including power, performance, thermal, reliability and variability. SEELab's research efforts on this topic target to solve these problems, including renewable energy integration in large scale systems, individual load energy reduction and automation, energy storage, context-aware energy management for smart devices, user activity modeling, smart grid pricing and load integration. To solve these problems, we design and implement multiple tools that not only model and analyze smaller individual pieces but also create a comprehensive representation of this vast environment.

Past Research:


Long-term research requiring high-resolution sensor data need platforms large enough to house solar panels and batteries. Leveraging a well-defined sensor appliance created using Sensor-Rocks, we develop novel context-aware power management algorithms to maximize network lifetime and provide unprecedented capability on miniaturized platforms.

Energy Efficient Routing and Scheduling For Ad-Hoc Wireless Networks

In large-scale ad hoc wireless networks, data delivery is complicated by the lack of network infrastructure and limited energy resources. We propose a novel scheduling and routing strategy for ad hoc wireless networks which achieves up to 60% power savings while delivering data efficiently. We test our ideas on a heterogeneous wireless sensor network deployed in southern California - HPWREN.


SHiMmer is a wireless platform that combines active sensing and localized processing with energy harvesting to provide long-lived structural health monitoring. Unlike other sensor networks that periodically monitor a structure and route information to a base station, our device acquires data and processes it locally before communicating with an external device, such as a remote controlled helicopter.

Event-driven Power Management

Power management (PM) algorithms aim at reducing energy consumption at the system-level by selectively placing components into low-power states. Formerly, two classes of heuristic algorithms have been proposed for power management: timeout and predictive. Later, a category of algorithms based on stochastic control was proposed for power management. These algorithms guarantee optimal results as long as the system that is power managed can be modeled well with exponential distributions. Another advantage is that they can meet performance constraints, something that is not possible with heuristics. We show that there is a large mismatch between measurements and simulation results if the exponential distribution is used to model all user request arrivals. We develop two new approaches that better model system behavior for general user request distributions. These approaches are event driven and give optimal results verified by measurements. The first approach is based on renewal theory. This model assumes that the decision to transition to low power state can be made in only one state. Another method we developed is based on the Time-Indexed Semi-Markov Decision Process model (TISMDP). This model allows for transitions into low power states from any state, but it is also more complex than our other approach. The results obtained by renewal model are guaranteed to match results obtained by TISMDP model, as both approaches give globally optimal solutions. We implemented our power management algorithms on two different classes of devices and the measurement results show power savings ranging from a factor of 1.7 up to 5.0 with insignificant variation in performance.

Energy-efficient software design

Time to market of embedded software has become a crucial issue. As a result, embedded software designers often use libraries that have been preoptimized for a given processor to achieve higher code quality. Unfortunately, current software design methodology often leaves high-level arithmetic optimizations and the use of complex library elements up to the designers' ingenuity. We present a tool flow and a methodology that automates the use of complex processor instructions and pre-optimized software library routines using symbolic algebraic techniques. It leverages our profiler that relates energy consumption to the source code and allows designers to quickly obtain energy consumption breakdown by procedures in their source code.

Energy-efficient wireless communication

Today’s wireless networks are highly heterogeneous with diverse range requirements and QoS. Since the battery lifetime is limited, power management of the communication interfaces without any significant degradation in performance has become essential. We show a set of different approaches that efficiently reduce power consumption under different environments and applications.When multiple wireless network interfaces (WNICs) are available, we propose a policy to decides what WNIC to employ for a given application and how to optimize the its usage leading to a large improvement in power savings. In the case of client-server multimedia applications running on wireless portable devices, we can exploit the server knowledge of the workload. We present a client- and a server-PM that by exchanging power control information can achieve more than 67 % with no performance loss. Wireless communication represents a critical aspect also in the design of specific applications such as distributed speech recognition in portable devices. We consider quality-of-service tradeoffs and overall system latency and present a wireless LAN scheduling algorithm to minimize the energy consimption of a distributed speech recognition front-end.