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2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT) (2010)
Vienna, Austria
Sept. 11, 2010 to Sept. 15, 2010
ISBN: 978-1-5090-5032-1
pp: 181-192
Ganesh Dasika , Advanced Computer Architecture Laboratory, University of Michigan - Ann Arbor, USA
Ankit Sethia , Advanced Computer Architecture Laboratory, University of Michigan - Ann Arbor, USA
Vincentius Robby , Advanced Computer Architecture Laboratory, University of Michigan - Ann Arbor, USA
Trevor Mudge , Advanced Computer Architecture Laboratory, University of Michigan - Ann Arbor, USA
Scott Mahlke , Advanced Computer Architecture Laboratory, University of Michigan - Ann Arbor, USA
ABSTRACT
Medical imaging provides physicians with the ability to generate 3D images of the human body in order to detect and diagnose a wide variety of ailments. Making medical imaging portable and more accessible provides a unique set of challenges. In order to increase portability, the power consumed in image acquisition - currently the most power-consuming activity in an imaging device - must be dramatically reduced. This can only be done, however, by using complex image reconstruction algorithms to correct artifacts introduced by low-power acquisition, resulting in image processing becoming the dominant power-consuming task. Current solutions use combinations of digital signal processors, general-purpose processors and, more recently, general-purpose graphics processing units for medical image processing. These solutions fall short for various reasons including high power consumption and an inability to execute the next generation of image reconstruction algorithms. This paper presents the MEDICS architecture - a domain-specific multicore architecture designed specifically for medical imaging applications, but with sufficient generality tomake it programmable. The goal is to achieve 100 GFLOPs of performance while consuming orders of magnitude less power than the existing solutions. MEDICS has a throughput of 128 GFLOPs while consuming as little as 1.6W of power on advanced CT reconstruction applications. This represents up to a 20X increase in computation efficiency over current designs.
INDEX TERMS
Stream processing, Low power, Medical imaging, Operation chaining, Stacked DRAM
CITATION
Ganesh Dasika, Ankit Sethia, Vincentius Robby, Trevor Mudge, Scott Mahlke, "MEDICS: Ultra-portable processing for medical image reconstruction", 2010 19th International Conference on Parallel Architectures and Compilation Techniques (PACT), vol. 00, no. , pp. 181-192, 2010, doi:
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