Logic cloning based approximate signed multiplication circuits for FPGA
Bibliographic record
Abstract
As hardware circuits become larger and more intricate, there’s a growing need for approximate circuit techniques. These approaches offer a trade-off, sacrificing some system accuracy in exchange for greater hardware resource efficiency and energy conservation. In the context of FPGA-based computation-intensive arithmetic multiplication, Logic Cloning (LC) is introduced to systematically induce controlled approximation. LC-Baugh Wooley (BW) circuits deliver exceptional error performance with precise approximation, while LC-Booth circuits are characterized by reduced Look-Up Table (LUT) resource consumption. In the case of 16-bit operands, LC methods effectively reduce LUT resource consumption by 31.05% for Booth and 36.85% for BW. Additionally, compared to their accurate counterparts, they lower the Power Delay Product (PDP) by 34% for Booth and 35% for BW. When it comes to symbol error-rate performance for Zero Forcing (ZF) Multiple-Input-Multiple-Output (MIMO) uplink detection, these LC approximate multiplication circuits exhibit robust performance, particularly LC-BW circuits, which closely match the accuracy of ZF detection, followed by LC-Booth circuits.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".