Zhijia ZhaoAssociate ProfessorComputer Science & Engineering zhijia@ucr.edu(951) 827-2993
CRII: SHF: FSM-Centric Approximate Computing - A Disciplined Approach
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AWARD NUMBER
008052-002
FUND NUMBER
33234
STATUS
Closed
AWARD TYPE
3-Grant
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AWARD EXECUTION DATE
2/16/2016
BEGIN DATE
3/1/2016
END DATE
2/28/2018
AWARD AMOUNT
$175,000
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Sponsor Information
SPONSOR AWARD NUMBER
SPONSOR
SPONSOR TYPE
FUNCTION
Organized Research
PROGRAM NAME
Proposal Information
PROPOSAL NUMBER
16030263
PROPOSAL TYPE
New
ACTIVITY TYPE
Basic Research
PI Information
PI
Zhao, Zhijia
PI TITLE
Other
PI DEPTARTMENT
Computer Science & Engineering
PI COLLEGE/SCHOOL
Bourns College of Engineering
CO PIs
Project Information
ABSTRACT
This project proposes a new paradigm to enhance computing efficiency --- Finite State Machine (FSM)-centric approximate computing. Approximate computing has shown promise for both reducing energy consumption and improving performance across different applications, especially those in image processing, machine learning and data analytics. To date, approximate computing has been inapplicable to FSM modeling of computations, which has important applications in domains that include biological science, cyber security, data compression, software engineering and hardware design. Growing data volumes and limitations on computer processing power constrain FSM?s efficiency. The establishment of FSM-centric approximate computing will open the door to a new dimension of efficiency optimization for software applications.
This research will take advantage of the synergy between FSM computations and approximate computing --- the inherent error tolerance capability within FSM computations --- to develop a computing platform for exploring approximate FSM computations. The key idea is a quantitative analysis of FSM reliability that captures how errors generated by underlying approximate hardware propagate through FSM transitions. Additionally, this research will also design and implement two complementary approximation schemes --- one relies on the "inexactness" of approximate hardware; the other provides pure software approximation and runs on conventional exact hardware. Together these approximation strategies will demonstrate the potential of FSM-centric approximate computing in improving the efficiency of FSM applications.(Abstract from NSF)
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