Bibliographic record
Abstract
Large-scale deployment of field-programmable gate arrays (FPGAs) into datacenters has introduced new use cases that utilize the FPGA as a multi-user compute platform, require more on-chip memory, and can benefit even more from efficient hard blocks. Since FPGAs are not designed for this new use case, the burden of efficiently utilizing existing FPGAs for datacenter applications is on datacenter service providers and application designers. We propose to reduce this burden by optimizing the architecture of FPGAs to be more efficient for the emerging datacenter applications and create computer-aided design (CAD) tools that facilitate architecture modifications of FPGAs in general. In this thesis, we first create a CAD tool capable of automatic block RAM (BRAM) modelling and optimization for SRAM and MTJ memory technologies. We use this CAD tool to quantify the benefits of MTJ memory technology in BRAM design at both the block and system levels. In addition, we introduce COFFE 2, an automatic circuit modelling tool for heterogeneous FPGAs capable of accurately modelling arbitrary heterogeneous blocks using a proper mix of full custom and standard cell design flows. In addition, COFFE 2 can model complex fracturable logic tiles enhanced with hard arithmetic and can automatically floorplan them similarly to industrial practice. Next, we investigate virtualized FPGA designs that are more efficient at distributing data to one or more applications both in current FPGAs and in hard network-on-chip-enhanced FPGAs. Finally, we quantify the costs of keeping user data confidential against different threat levels in virtualized FPGAs, both in current FPGAs and in new architectures that we propose.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".