Determining Application-specific Knowledge for Improving Robustness of Sequential Circuits

Sebastian Huhn, Stefan Frehse, Robert Wille, Rolf Drechsler

Research output: Contribution to journalArticlepeer-review

Abstract

Due to their shrinking feature sizes as well as environmental influences such as high-energy radiation, electrical noise, particle strikes, etc., integrated circuits are getting more vulnerable to transient faults. Accordingly, how to make those circuits more robust has become an essential step in today’s design flows. Methods increasing the robustness of circuits against these faults already exist for a long period of time but either introduce huge additional logic, change the timing behavior of the circuit, or are applicable for dedicated circuits such as microprocessors only. In this work, we propose an alternative method which overcomes these drawbacks by determining application-specific knowledge of the circuit, namely the relations of Flip Flops and when they assume the same value. By this, we exploit partial redundancies, which are inherent in most circuits anyway (even the optimized ones), to frequently compare circuit signals for their correctness – eventually leading to an increased robustness. Since determining the correspondingly needed information is a computationally hard task, formal methods such as Bounded Model Checking, SAT-based Automatic Test Pattern Generation, and Binary Decision Diagrams are utilized for this purpose. The resulting methodology requires only a slight increase in additional hardware, does only influence the timing behavior of the circuit negligibly, and is automatically applicable to arbitrary circuits. Experimental evaluations confirm these benefits.
Original languageEnglish
Article number8618608
Number of pages13
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
DOIs
Publication statusPublished - 2018

Fields of science

  • 102 Computer Sciences
  • 202 Electrical Engineering, Electronics, Information Engineering

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Mechatronics and Information Processing
  • Nano-, Bio- and Polymer-Systems: From Structure to Function

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