Bibliographic record
Abstract
Reconfigurable compute devices, namely Field Programmable Gate Arrays (FPGA), have increasingly been deployed in datacenters and cloud infrastructures. Such devices have proven effective at performing certain types of compute tasks, often performing these tasks faster, with lower latency, at a higher throughput, and/or at lower power than traditional compute devices (e.g. CPUs). Some recent works have demonstrated the benefits of deploying such devices as direct-connected nodes, i.e., the FPGAs are connected directly to the datacenters' network infrastructure. In this work, we introduce the concept of the Network Management Unit (NMU), which secures the network from potentially unwarranted access from malicious or malfunctioning FPGA applications; specifically, the NMU targets network traffic originating from (or targeted towards) an application resident solely on the FPGA itself. We argue that this is a necessary feature of direct-connected reconfigurable compute devices. We present the design of several NMUs, introduce a taxonomy for describing these different designs, and analyze the trade-offs of each design. The NMUs discussed range from those that employ hairpin routing techniques to push management to the next level switch, to those that perform routing and access control directly.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".