Determining application-specific peak power and energy requirements for ultra-low-power processors

Ye, Weidong

Permalink

https://hdl.handle.net/2142/97289 Copy

Description

Title

Determining application-specific peak power and energy requirements for ultra-low-power processors

Author(s)

Ye, Weidong

Issue Date

2017-03-29

Director of Research (if dissertation) or Advisor (if thesis)

Kumar, Rakesh

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Date of Ingest

2017-08-10T19:14:38Z

Keyword(s)

• Peak power
• Peak energy
• Internet of Things (IoT)

Abstract

Many emerging applications such as IoT, wearables, implantables, and sensor networks are power- and energy-constrained. These applications rely on ultra-low-power processors that have rapidly become the most abundant type of processor manufactured today. In the ultra-low-power embedded systems used by these applications, peak power and energy requirements are the primary factors that determine critical system characteristics, such as size, weight, cost, and lifetime. While the power and energy requirements of these systems tend to be application-speci c, conventional techniques for rating peak power and energy cannot accurately bound the power and energy requirements of an application running on a processor, leading to overprovisioning that increases system size and weight. In this thesis, we present an automated technique that performs hardware-software co-analysis of the application and ultra-low-power processor in an embedded system to determine application-speci c peak power and energy requirements.Our technique provides more accurate, tighter bounds than conventional techniques for determining peak power and energy requirements, reporting 15% lower peak power and 17% lower peak energy, on average, than a conventional approach based on pro ling and guardbanding. Compared to an aggressive stressmark-based approach, our technique reports power and energy bounds that are 26% and 26% lower, respectively, on average. Also, unlike conventional approaches, our technique reports guaranteed bounds on peak power and energy independent of an application's input set. Tighter bounds on peak power and energy can be exploited to reduce system size, weight, and cost.

Graduation Semester

2017-05

Type of Resource

text

Permalink

http://hdl.handle.net/2142/97289

Copyright and License Information

Copyright 2017 Weidong Ye

Owning Collections