Comparing performance, productivity and scalability of the TILT overlay processor to OpenCL HLS
Why this work is in the frame
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Bibliographic record
Abstract
High-Level-Synthesis (HLS) tools translate a software description of an application into custom FPGA logic, increasing designer productivity vs. Hardware Description Language (HDL) design flows. Overlays seek to further improve productivity by reducing application compile times and raising abstraction by enabling the designer to target a software-programmable substrate instead of the underlying FPGA. We compare the performance, development effort and scalability of two C-to-FPGA approaches: our TILT overlay processor and Altera's OpenCL HLS. Our application-customized TILT implementations of five data-parallel benchmarks have from 41 % to 80% of the throughput per unit of layout area achieved by our best OpenCL HLS designs. The time required for initial hardware compilation of these TILT designs and configuration of the target application onto the overlay is roughly comparable to the compile times of the OpenCL HLS designs: 28 and 103 minutes on average respectively. However subsequent reconfigurations due to changes in the application that do not require re-synthesis of the overlay are fast, taking 38 seconds on average. In contrast, OpenCL HLS applications require full recompilation after every code change. TILT also enables smaller, more area-efficient designs than OpenCL HLS when low to moderate throughput is sufficient. For high throughput, the larger spatially pipelined designs of OpenCL HLS are preferable.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it