UCLA

Recently, a broad range of ENS applications have appeared for large-scale systems, introducing new requirements leading to new embedded architectures, associated algorithms, and supporting software systems. These new requirements include the need for diverse and complex sensor systems that present demands for energy and computational resources as well as for broadband communication. To satisfy application demands while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. In this thesis we described the LEAP design approach, in which the system is able to adaptively select the most energy efficient hardware components matching an application's needs. The LEAP approach supports highly dynamic requirements in sensing fidelity, computational load, storage media, and network bandwidth. It focuses on episodic operation of each component and considers the energy dissipation for each platform task by integrating fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems, including sensors. In addition to LEAP's unique hardware capabilities, its software architecture has been designed to provide an easy to use power management interface, a robust, fault tolerant operating environment, and to enable remote upgrade of individual software components.

Current research topics such as mobile computing and embedded networked sensing (ENS) have been addressing energy efficiency as a cornerstone necessity, due to their requirement for portability and long battery life times. This thesis discusses one such related project that, while currently directed toward ENS computing applications, is generally applicable to a wide ranging set of applications including both mobile and enterprise computing. While relevant to many applications, it is focuses on ENS environments necessitating high performance computing, networking, and storage systems while maintaining low average power operations.