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Data-Stream-Driven Computers are Power and Energy Efficient

Data-Stream-Driven Computers are Power and Energy Efficient

Abdelghani Renbi
Copyright: © 2012 |Pages: 16
ISBN13: 9781466618428|ISBN10: 1466618426|EISBN13: 9781466618435
DOI: 10.4018/978-1-4666-1842-8.ch016
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MLA

Naima Kaabouch and Wen-Chen Hu. "Data-Stream-Driven Computers are Power and Energy Efficient." Energy-Aware Systems and Networking for Sustainable Initiatives, IGI Global, 2012, pp.361-376. https://doi.org/10.4018/978-1-4666-1842-8.ch016

APA

N. Kaabouch & W. Hu (2012). Data-Stream-Driven Computers are Power and Energy Efficient. IGI Global. https://doi.org/10.4018/978-1-4666-1842-8.ch016

Chicago

Naima Kaabouch and Wen-Chen Hu. "Data-Stream-Driven Computers are Power and Energy Efficient." In Energy-Aware Systems and Networking for Sustainable Initiatives. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1842-8.ch016

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Abstract

It is believed that data-stream-driven computing is power and energy efficient as compared to its counterpart, instruction-stream-driven computing. This latter requires memory access and memory control overheads while the processor is fetching task instructions from the memory. The programmer describes all the tasks as instructions in the program memory. On the other hand data-stream-driven computer is already configured or hardwired for a specific computing operation, no memory is required apart from data storage. In some contexts we refer to data-stream-driven computers as accelerators or single-purpose processors. This chapter discusses the benefit of data-stream-driven computing for better power and energy efficiency. We took matrix multiplication as an example application to compare the power and energy dissipations between load/store and non-instruction fetch-based architectures. We witnessed that single-purpose processor reduces almost 100% of the dynamic power when replacing the general-purpose processor. With the current mainstream transistor technology, morphware platforms that allow massive parallelism are the potential key for data-stream-driven computer implementations to saving energy in battery-powered embedded systems and to solve the dissipated power dilemma, as the heat becomes the bottleneck of traditional high frequency processors. If the same strategy is applied to mainstream computers and data center servers, we will not only reduce electricity bills but we will also contribute to greener computing by lowering the IT sector’s CO2 emissions.

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