RedMulE-FT: A Reconfigurable Fault-Tolerant Matrix Multiplication Engine
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912337505550336 |
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| author | Wiese, Philip Item, Maurus Bertaccini, Luca Tortorella, Yvan Garofalo, Angelo Benini, Luca |
| author_facet | Wiese, Philip Item, Maurus Bertaccini, Luca Tortorella, Yvan Garofalo, Angelo Benini, Luca |
| contents | As safety-critical applications increasingly rely on data-parallel floating-point computations, there is an increasing need for flexible and configurable fault tolerance in parallel floating-point accelerators such as tensor engines. While replication-based methods ensure reliability but incur high area and power costs, error correction codes lack the flexibility to trade off robustness against performance. This work presents RedMulE-FT, a runtime-configurable fault-tolerant extension of the RedMulE matrix multiplication accelerator, balancing fault tolerance, area overhead, and performance impacts. The fault tolerance mode is configured in a shadowed context register file before task execution. By combining replication with error-detecting codes to protect the data path, RedMulE-FT achieves an 11x uncorrected fault reduction with only 2.3% area overhead. Full protection extends to control signals, resulting in no functional errors after 1M injections during our extensive fault injection simulation campaign, with a total area overhead of 25.2% while maintaining a 500 MHz frequency in a 12 nm technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RedMulE-FT: A Reconfigurable Fault-Tolerant Matrix Multiplication Engine Wiese, Philip Item, Maurus Bertaccini, Luca Tortorella, Yvan Garofalo, Angelo Benini, Luca Hardware Architecture B.7.3; B.8.1 As safety-critical applications increasingly rely on data-parallel floating-point computations, there is an increasing need for flexible and configurable fault tolerance in parallel floating-point accelerators such as tensor engines. While replication-based methods ensure reliability but incur high area and power costs, error correction codes lack the flexibility to trade off robustness against performance. This work presents RedMulE-FT, a runtime-configurable fault-tolerant extension of the RedMulE matrix multiplication accelerator, balancing fault tolerance, area overhead, and performance impacts. The fault tolerance mode is configured in a shadowed context register file before task execution. By combining replication with error-detecting codes to protect the data path, RedMulE-FT achieves an 11x uncorrected fault reduction with only 2.3% area overhead. Full protection extends to control signals, resulting in no functional errors after 1M injections during our extensive fault injection simulation campaign, with a total area overhead of 25.2% while maintaining a 500 MHz frequency in a 12 nm technology. |
| title | RedMulE-FT: A Reconfigurable Fault-Tolerant Matrix Multiplication Engine |
| topic | Hardware Architecture B.7.3; B.8.1 |
| url | https://arxiv.org/abs/2504.14399 |