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Energy Efficient Computing with the Low Power, Energy Aware Processing (LEAP) Architecture

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What
  • PhD Defenses
When Dec 03, 2012
from 08:00 AM to 10:00 AM
Where Faraday Room, 67-124 Engr IV
Contact Name
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Abstract

Recently, a broad range of embedded network sensing 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 seminar 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. 

 

Biography

Dustin McIntire is a Ph.D. candidate in UCLA’s Electrical Engineering Department, conducting research in the ASCENT lab.  He earned his B.S. from Stanford University and M.S. from UCLA in electrical engineering.  He has worked as a professional engineer for over 15 years in numerous embedded sensing applications.  Dustin’s research focuses on creating energy efficient systems through high fidelity, in-situ energy measurement of hardware and software systems.

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