The Costs of Confidentiality in Virtualized FPGAs
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
Some modern datacenters are augmenting their compute infrastructure by deploying field-programmable gate arrays (FPGAs) to provide users with specialized accelerators that offer superior compute capability, increased energy efficiency, lower latency, and more programming flexibility than CPUs. However, the higher programming flexibility of FPGAs also gives more capabilities to malicious users to remotely sniff data from other applications running on the same FPGA. This has created a challenge for efficient utilization of FPGAs in datacenters: FPGAs in datacenters are currently not shared between users due to potential security risks. In this paper, we propose different techniques to defeat data-sniffing attacks in datacenter FPGAs by encrypting/decrypting the user application's data. We describe techniques that are appropriate to different trust levels and rigorously evaluate the costs of these data confidentiality techniques in current virtualized FPGAs. In addition, for each trust level, we propose an architectural change to the FPGA to mitigate the costs of providing data confidentiality. We also investigate the role of interconnect in these architectural changes and demonstrate that more efficient security features can be implemented together with the interconnect if the FPGAs use a hard network on chip.
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 | 0.001 |
| 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 | 0.000 |
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