Software-based Dynamic Overlays Require Fast, Fine-grained Partial Reconfiguration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we consider dynamic overlays which use fine-grained partial reconfiguration (PR) to continuously adapt to their software-based workload. In particular, we show how to modify a traditional (static) overlay developed for OpenVX into a dynamic overlay. We use a Xilinx FPGA, and show that the dynamic overlay needs unsupported features including faster PR, relocatability, and fine-grained configuration is needed for performance. Since these features are not available in Xilinx FPGAs, we estimate the application-level speedup they would provide. We find that vector custom instruction (VCI) chaining, which allow a VCI to directly cascade its result into another VCI is also essential. Overall, we find the static overlay achieves a speedup of roughly 20x faster than a Cortex-A9 processor, but with improved PR and chaining a speedup of 106x is attainable. While there have been calls for fast, fine-grained PR devices for decades, we believe that dynamic overlays may be the first true "killer application" that will justify adding these features to all FPGA devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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