PoP-ECC: Robust and Flexible Error Correction against Multi-Bit Upsets in DNN Accelerators
Taewon Park, Saeid Gorgin, Dongwhee Kim, Jaeho Shin, Michael B. Sullivan, Jungrae Kim
摘要
Deep Neural Networks (DNNs) in safety-critical systems require high reliability. Many systems deploy Error Correction Codes (ECCs) to protect DNNs from memory errors. However, continuous process scaling increases memory errors in severity and frequency, necessitating strong protection against Multi-Bit Upsets (MBUs). This paper proposes Parities of Parities ECC (PoP-ECC), a novel two-tier memory protection scheme designed to provide robust, efficient, and flexible protection against MBUs. PoP-ECC generates Virtual Parities (VPs), which are used to compute secondlevel parities called Parities of Parities (PPs). This two-level ECC structure allows for dynamic error correction tailored to varying error patterns, ensuring system reliability with minimal memory overhead. Our evaluation demonstrates that PoP-ECC can tolerate significantly higher MBU ratios compared to state-of-the-art solutions, with negligible delay, area, and power overhead.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
引用它的顶会 Paper1
问问它们各自怎么用它相关 Paper
- DBC: Drift-aware Binary Code for Drift-tolerant Deep Neural NetworksInsu Choi, Jaeyong Chung, Joon-Sung YangDAC 2025
- Polymorphic Error CorrectionEvgeny Manzhosov, Simha SethumadhavanMICRO 2024 · 被引用 1 次
- Bipolar vector classifier for fault-tolerant deep neural networksSuyong Lee, Insu Choi, Joon-Sung YangDAC 2022 · 被引用 4 次
- EPIC: Error PredIction and Correction for Power-Efficient Voltage Underscaling Multiply-Accumulate UnitTongjing Wu, Xiaolu Hu, Tong Li, Siting Liu 等DAC 2025 · 被引用 1 次
- Structural Coding: A Low-Cost Scheme to Protect CNNs from Large-Granularity Memory FaultsAli Asgari Khoshouyeh, Florian Geissler, Syed Sha Qutub, Michael Paulitsch 等SC 2023 · 被引用 8 次
