MAPLE: A Machine Learning based Aging-Aware FPGA Architecture Exploration Framework
Bibliographic record
Abstract
In this paper, we develop a framework called MAPLE to enable the aging-aware FPGA architecture exploration. The core idea is to efficiently model the aging-induced delay degradation at the coarse-grained FPGA basic block level using deep neural networks (DNNs). For each type of the FPGA basic block such as LUT and DSP, we first characterize its accurate delay degradation via transistor-level SPICE simulation under a versatile set of aging factors from the FPGA fabric and in-field operation. Then we train one DNN model for each block type to quickly and accurately predict the complex relation between its delay degradation and comprehensive aging factors. Moreover, we integrate our DNN models into the widely used Verilog-to-Routing toolflow (VTR 8) to support analyzing the impact of aging-induced delay degradation on the entire large-scale FPGA architecture. Experimental results demonstrate that MAPLE can predict the delay degradation of FPGA blocks 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> times faster than transistor-level SPICE simulation, with a prediction error less than 0.7%. Our case study demonstrates that FPGA architects can effectively leverage MAPLE to explore better aging-aware FPGA architectures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".