Logic cloning based approximate signed multiplication circuits for FPGA
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.001 |
| 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 it