Analyzing the Vulnerabilities of External SDRAM on System-on-Chip Field Programmable Gate Array Devices
Bibliographic record
Abstract
System-on-chip (SoC) field programmable gate array (FPGA) devices are becoming increasingly prominent in a vast range of applications. The fusion of the FPGA’s unmatched parallel computing capacity and flexibility with a full-bore processing system makes these devices extremely powerful. With recent technological progress, SoC FPGA devices are implemented in increasingly complex systems where security and safety are often issues of concern. To cater to these concerns, these devices are commonly fit with encryption and authentication capabilities to ensure the confidentiality and authenticity of externally stored bitstreams, firmware, and bootloaders. However, while much effort is placed into securing these partitions when stored in external memory, little attention seems to be paid to the security of this data once it is decrypted for execution. This article investigates how vulnerable systems are to attacks that target decrypted data during execution. We demonstrate that data stored in external synchronous dynamic random access memory (SDRAM) can provide access to trusted and secured interfaces of SoC FPGA devices even with diligently applied security features.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".