Quantifying and mitigating the costs of FPGA virtualization
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
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.
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 | 0.000 | 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) | 0.000 | 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