Auto Generation of High-Performance Fixed-Point Multiplier for Artificial Neural Networks
Bibliographic record
Abstract
Multiplier is a critical building block in artificial neural network (ANN). The precision and connection structure of the multiplier should be optimized for an ANN to achieve the best energy, speed and area efficiency. Changes in ANN application and CMOS process often result in the redesign of the multiplier. This paper presents an auto generation method for high-performance fixed-point multiplier based on three techniques, i.e. Modified Booth Encoding (MBE) scheme, improved three-dimensional reduction method (ITDM) and mixed parallel pipelining (MPP). The MBE is customized for ReLU activation function based ANN to remove the sign bit of the multiplicand to save area. The ITDM further shorts the critical path by changing the position of half adder in the conventional TDM. The proposed MPP divides the structures into different stages for parallel and pipelined implementation. The auto generated multiplier speed is 4.04 times faster and the layout is 29% denser and more regular than the conventional MBE combining with TDM method based multiplier.
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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 | 0.000 |
| 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) | 0.002 | 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 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".