HashTag: Hash-based Integrity Protection for Tagged Architectures
Lukas Lamster, Martin Unterguggenberger, David Schrammel, Stefan Mangard
摘要
Modern computing systems rely on error-correcting codes to ensure the integrity of DRAM data. Linear checksums allow for fast detection and correction of specific error patterns. However, they do not offer sufficient protection against complex errors distributed over multiple data words and chips. Depending on the code and the error pattern, linear codes may fail to detect or even miscorrect errors, thus leading to silent data corruption. In this work, we show how compact error-correcting codes based on low-latency hashing functions allow for strong probabilistic error detection and correction while facilitating ECC bit repurposing. Our proposed design drastically lowers the expected rate of undetected errors, regardless of the underlying error patterns. By tailoring the size of our codes to the required level of integrity protection, we are able to free bits that would otherwise be required to store ECC data. We showcase how our design facilitates the efficient implementation of tagged memory architectures such as CHERI, ARM MTE, and SPARC ADI by repurposing the freed bits in commodity ECC DRAM. Thus, we harden systems against data corruption due to DRAM faults while simultaneously allowing for memory tagging without introducing additional memory accesses. We present a systematic analysis of schemes that allow memory tagging on a cache line granularity while maintaining error detection and correction capabilities, even in multi-bit fault scenarios. We evaluate our integrity protection with tagging for different use cases and show that we can store 32 bits of additional tags per cache line, twice the amount needed to implement ARM's MTE, without significantly affecting error correction capabilities. We also show how up to 51 bits can be made available while maintaining single-bit error correction.
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引用它的顶会 Paper4
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它引用的顶会 Paper7
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- HDFI: Hardware-Assisted Data-Flow IsolationChengyu Song, Hyungon Moon, Monjur Alam, Insu Yun 等S&P 2016 · 被引用 146 次
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