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research article

Convolution Engine: Balancing Efficiency and Flexibility in Specialized Computing

Qadeer, Wajahat
•
Hameed, Rehan
•
Shacham, Ofer
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2015
Communications of the ACM (CACM)

General-purpose processors, while tremendously versatile, pay a huge cost for their flexibility by wasting over 99% of the energy in programmability overheads. We observe that reducing this waste requires tuning data storage and compute structures and their connectivity to the data-flow and data-locality patterns in the algorithms. Hence, by backing off from full programmability and instead targeting key data-flow patterns used in a domain, we can create efficient engines that can be programmed and reused across a wide range of applications within that domain. We present the Convolution Engine (CE)—a programmable processor specialized for the convolution-like data-flow prevalent in computational photography, computer vision, and video processing. The CE achieves energy efficiency by capturing data-reuse patterns, eliminating data transfer overheads, and enabling a large number of operations per memory access. We demonstrate that the CE is within a factor of 2–3× of the energy and area efficiency of custom units optimized for a single kernel. The CE improves energy and area efficiency by 8–15× over data-parallel Single Instruction Multiple Data (SIMD) engines for most image processing applications.

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Type
research article
DOI
10.1145/2735841
Author(s)
Qadeer, Wajahat
•
Hameed, Rehan
•
Shacham, Ofer
•
Venkatesan, Preethi
•
Kozyrakis, Christos  
•
Horowitz, Mark
Date Issued

2015

Published in
Communications of the ACM (CACM)
Volume

58

Issue

4

Start page

85

End page

93

Subjects

Specialization

•

energy efficiency

•

video processing

•

accelerator

Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
ISC  
Available on Infoscience
September 23, 2015
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/118578
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