Quantifying and mitigating the costs of FPGA virtualization
Bibliographic record
Abstract
FPGAs are being incorporated into contemporary datacenters in order to improve computational capacity, power consumption, and processing latency. Efficiently integrating FP-GAs in datacenters is, however, quite challenging. Ideally, smaller tasks could share a device and the cloud management layer would be able to partially reconfigure the device to allocate its free resources to incoming tasks. Moreover, to facilitate FPGA hardware upgrades without undue porting effort for previously developed accelerator tasks, the complexities associated with board-specific system-level integration should be abstracted away from designers. By meeting these requirements, FPGAs in the cloud would become multi-user virtualized resources with increased availability and elasticity. The virtualization of FPGAs, however, comes with two major costs in current FPGAs: lower application operating frequency, and extravagant use of routing resources. In this paper, we quantify the costs of FPGA virtualization and demonstrate that for an FPGA that supports four independent tasks, virtualization reduces the task average frequency by 18% to 46% and increases wire usage to 2.6×. We also investigate the cause of these costs and show that the use of hard NoCs in future datacenter-optimized FPGAs would facilitate FPGA virtualization without sacrificing operating frequency or routing resources.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".