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LEXACT: low energy n-modular redundancy using approximate computing for real-time multicore processors

Baharvand, F ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TETC.2017.2737045
  3. Publisher: IEEE Computer Society , 2020
  4. Abstract:
  5. Multicore processors are becoming popular in safety-critical applications. A series of these applications comprises of kernels where inexact computations may produce results within the boundary of sufficient quality though, for which the reliability should stay at the maximum possible level. Intrinsic core-level redundancy in multicore processors can be leveraged to achieve the desired reliability level in form of N-modular redundancy (NMR). While NMR provides a proactive means of reliability for critical systems, it has two main drawbacks: Increase in the area and energy consumption that are both limiting factors in the embedded systems. This paper presents a software-based method to construct NMR in a multicore processor through k cores using approximate computing concept, where k < N. The method uses a combination of exact and approximate versions of a task to run on different cores in order to increase the reliability. To identify the suitable approximation factor, a tool called LEXACT has been developed to process the target applications. The method reduces the energy consumption and area overheads of the NMR as compared to its traditional implementation. Experimental results show at least 35 percent reduction in the energy consumption, and some 40 percent area reduction while achieving the desired reliability level and quality of result. © 2013 IEEE
  6. Keywords:
  7. Approximate computing ; Multicore processors ; N-modular redundancy ; Embedded systems ; Energy utilization ; Green computing ; Nuclear magnetic resonance ; Program processors ; Real time systems ; Redundancy ; Reliability ; Safety engineering ; Approximate computing ; Multi-core processing ; Multi-core processor ; N-modular redundancies ; Real-time embedded systems ; Multicore programming
  8. Source: IEEE Transactions on Emerging Topics in Computing ; Volume 8, Issue 2 , 2020 , Pages 431-441
  9. URL: https://ieeexplore.ieee.org/document/8003502