A High-Level Synthesis Methodology for Energy and Reliability-Oriented Designs

Dilek S., Smri R., Tosun S., Dal D.

IEEE Transactions On Computers, cilt.71, sa.1, ss.161-174, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 71 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/tc.2020.3043885
  • Dergi Adı: IEEE Transactions On Computers
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.161-174
  • Anahtar Kelimeler: Reliability, Integrated circuit reliability, Reliability engineering, Transient analysis, Energy consumption, Transistors, Digital systems, High-Level Synthesis (HLS), voltage islands, reliability, soft errors, energy, metaheuristic
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Shrinking technology sizes of the CMOS circuits makes it possible to place more transistors on a single chip at each technology generation. On the other hand, circuits become more vulnerable to radiation effects due to lower supply and threshold voltage levels; thus, the number of transient faults in circuits tends to increase. Moreover, energy reduction techniques also negatively affect the reliability of circuits. Traditional high-level synthesis (HLS) methods usually consider only area and latency along with either energy or reliability. Especially the effect of using different voltage levels on reliability is completely ignored by previous studies. In this article, we present two new HLS methods for application-specific integrated circuit (ASIC) design under area and timing constraints with the objectives of low energy consumption and high reliability. For the mapping and scheduling steps of HLS, we propose integer linear programming (ILP) and genetic algorithm (GA)-based optimization methods. While ILP provides the optimum results, the CPU time increases exponentially with the number of application nodes. On the other hand, GA-based metaheuristic is faster and determines optimum or near-optimum results in shorter times than ILP. Additionally, we use a selective duplication method to further improve the overall reliability.