TITLE: A Pipelined, Single Precision Floating-Point Logarithm Computation Unit in Hardware
Bibliographic record
Abstract
LEGAL DISCLAIMER: This is an academic research report. I, my supervisor, defence committee, and university, make no claim as to the fitness for any purpose, and accept no direct or indirect liability for the use of algorithms, findings, or recommendations in this thesis. ii A large number of scientific applications rely on the computing of loga-rithm. Thus, accelerating the speed of computing logarithms is significant and necessary. To this end, we present the realization of a pipelined Logarithm Computation Unit (LCU)1 in hardware that uses lookup table and interpo-lation techniques. The presented LCU supports single precision arithmetic with fixed accuracy and speed. We estimate that it can generate 2.9G single precision values per second under a 65nm fabrication process. In addition, the accuracy is at least 21 bits while lookup table size is about 7.776KB. To the best of our knowledge, our LCU achieves the fastest speed at its current accuracy and table size. 1This work is funded by the IBM Center for Advanced Studies iii iv Acknowledgments I would like to thank my supervisor Dr. Christopher Anand, for the trust and flexibility he gave me, which allowed me to do this project on my own f scratch. I would like to thank Robert Enenkel from the IBM Toronto Lab, for the literature he gave me, which motivated me for this project. I would also like to thank my parents, for their never ending support and encouragement. v vi
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".