Improving 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
FPGAs are being deployed in modern datacenters to provide users with specialized accelerators that offer superior compute capability, increased energy efficiency, lower latency, and more programming flexibility than CPUs. However, FPGAs are not utilized as efficiently in datacenters: unlike CPUs, FPGAs in datacenters are currently not shared between users due to potential security risks. The higher flexibility that comes with FPGAs also gives more capabilities to malicious users. Several recent studies have demonstrated examples of FPGA user applications capable of remotely sniffing data from other applications running on the same FPGA. In this work, we look at various ways to ameliorate these threats by encrypting/decrypting the user application's data under different trust levels for current virtualized FPGAs. We also discuss the role of interconnect and discuss the potential of more efficient security features that 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 | 0.000 | 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 | 0.001 |
| 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