Using multi-bit logic blocks and automated packing to improve field-programmable gate array density for implementing datapath circuits
Bibliographic record
Abstract
As the logic capacity of field-programmable gate arrays (FPGAs) increases, they are being increasingly used to implement large arithmetic-intensive applications, which often contain a large proportion of datapath circuits. Since datapath circuits usually consist of regularly structured components, called bit-slices, it is possible to utilize datapath regularity in order to achieve significant area savings through FPGA architectural innovations. This work describes such an FPGA logic block architecture, called a multi-bit logic block, which employs configuration memory sharing to exploit datapath regularity. It is experimentally shown that, comparing to conventional FPGA logic blocks, the multi-bit logic blocks can achieve 18% to 26% logic block area reduction for implementing datapath circuits, which represents an overall FPGA area saving of 5% to 13%. A packing algorithm for the multi-bit logic block architecture is also proposed in this paper; and it is used to empirically find the best values for several important architectural parameters of the new architecture, including the most area efficient granularity values and the most area efficient amount of configuration memory sharing.
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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 | 0.000 | 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 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".