Evaluation of Divider and Linear Interpolation Architectures on FPGAs
Bibliographic record
Abstract
The Field Programmable Gate Array (FPGA) is a platform with a unique set offeatures. It combines the programmability of general purpose computers with the flexibility of Application Specific Integrated Circuits (ASIC). Most basic operations have been thoroughly studied on ASICs and the best architecture for each operation has often been found. This is not the case for FPGAs where often it is just assumed that the best architecture for an operation is the same in a FPGA as in a ASIC. As FPGAs have unique features and restrictions compared to ASICs this assumption is not always right. In this thesis, divider- and interpolation-architectures have been studied and modified to fit better on the FPGA platform. To do this a base design from the ASIC world was taken and studied to look for things that can be improved for the FPGA platform. These changes were then simulated and tested on four different FPGA-chip series for a wide range of bit lengths. For the divider architecture, it was found that the non-restoring divider design performed the best. For the interpolation architecture, some interesting ideas on how to save hardware was found but no real conclusion can be reached about which design is better than the others.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".