High-speed and low-power reconfigurable architectures of 2-digit two-dimensional logarithmic number system-based recursive multipliers
Bibliographic record
Abstract
In new DSP applications, reconfigurable architectures have emerged to provide a flexible, high-performance, high-speed and low-power implementation platform for wireless embedded devices. Since some DSP algorithms rely heavily on multiplication, there are still demands for more efficient multiplication structures. In this study, two reconfigurable recursive multipliers are presented. The authors' architectures combine some of the flexibility of software with the high performance of hardware through implementing different levels of recursive multiplication schemes on a two-dimensional logarithmic number system (2DLNS) processing structure. The data are split into a number of smaller sections, where each section is converted to a 2-digit 2DLNS (2 bases) representation. The dynamic range reduction and logarithmic characteristics of computing with two orthogonal base exponents in this number system allows multiplication to be implemented with simple parallel small adders. The authors' architectures are able to perform single and double precision multiplications, as well as fault tolerant and dual throughput single precision operations. The implementations demonstrate the efficiency of 2DLNS in multiplication intensive DSP applications and show outstanding results in terms of operation delay and dynamic power consumption.
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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 | 0.000 |
| 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".