Title
Efficient data streaming with on-chip accelerators: Opportunities and challenges
Abstract
The transistor density of microprocessors continues to increase as technology scales. Microprocessors designers have taken advantage of the increased transistors by integrating a significant number of cores onto a single die. However, a large number of cores are met with diminishing returns due to software and hardware scalability issues and hence designers have started integrating on-chip special-purpose logic units (i.e., accelerators) that were previously available as PCI-attached units. It is anticipated that more accelerators will be integrated on-chip due to the increasing abundance of transistors and the fact that not all logic can be powered at all times due to power budget limits. Thus, on-chip accelerator architectures deserve more attention from the research community. There is a wide spectrum of research opportunities for design and optimization of accelerators. This paper attempts to bring out some insights by studying the data access streams of on-chip accelerators that hopefully foster some future research in this area. Specifically, this paper uses a few simple case studies to show some of the common characteristics of the data streams introduced by on-chip accelerators, discusses challenges and opportunities in exploiting these characteristics to optimize the power and performance of accelerators, and then analyzes the effectiveness of some simple optimizing extensions proposed.
Year
DOI
Venue
2011
10.1109/HPCA.2011.5749739
HPCA
Keywords
Field
DocType
research community,on-chip accelerator architecture,on-chip special-purpose logic unit,large number,data access stream,paper attempt,efficient data,data stream,research opportunity,on-chip accelerator,cryptography,chip,spectrum,system on a chip,engines,data access,coherence
Power budget,Data stream mining,System on a chip,Computer science,Cryptography,Parallel computing,Real-time computing,Software,Transistor,Data access,Embedded system,Scalability
Conference
ISSN
Citations 
PageRank 
1530-0897
15
0.68
References 
Authors
13
7
Name
Order
Citations
PageRank
Rui Hou14511.05
Lixin Zhang257145.96
Michael C. Huang387558.47
Kun Wang4332.38
Hubertus Franke51257104.86
Yi Ge6384.06
Xiaotao Chang7866.18