Fault-tolerant resynthesis with dual-output LUTs
Bibliographic record
Abstract
We present a fault-tolerant post-mapping resynthesis for FPGA-based designs that exploits the dual-output feature of modern FPGA architectures to improve the reliability of a mapped circuit against faults. Emerging FPGA architectures, such as 6-LUTs in Xilinx Virtex-5 and 8-input ALMs in Altera Stratix-III, have a secondary LUT output that allows access to non-occupied SRAM bits. We show that this architectural feature can be used to build redundancy for fault masking with limited area and performance over-head. Our algorithm improves reliability of a mapping by performing two basic operations: duplication (in which free configuration bits are used to duplicate a logic function whose value is obtained at the secondary output) and encoding (in which two copies of the same logic function are ANDed or ORed together in the fanout of the duplicated logic). The problem of fault tolerant post-mapping resynthesis is then formulated as the optimal duplication and encoding scheme that ensures the minimal circuit fault rate w.r.t. a stochastic single fault model. We present an ILP formulation of this problem and an efficient algorithm based on generalized network flow. On MCNC benchmarks, experimental results show that for combinational circuits the proposed approach improves meantime- to-failure(MTTF) by 27% with 4% area overhead, and the proposed approach with explicit area redundancy improves MTTF by 113% with 36% area overhead, compared to the baseline mapping by ABC. This provides a viable fault tolerance solution for non-mission critical applications compared to TMR (triple modular redundancy) which has a 5x--6x area overhead.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".