Arithmetic with the two-dimensional logarithmic number system (2dlns)
Bibliographic record
Abstract
The ever increasing demand for low power DSP applications has directed researchers to contemplate a variety of potential approaches in different contexts. Since DSP algorithms heavily rely on multiplication, there are growing demands for more efficient multiplication structures. In this regard, using some alternative number systems, which inherently are capable of reducing the hardware complexity, have been studied. The Multi-Dimensional Logarithmic Number System (MDLNS), a multi-digit and multi-base extension to the Logarithmic Number System (LNS), is considered as an alternative to the traditional binary representation for selected applications. The MDLNS provides a reduction in the size of the number representation with a non-linear mapping and promises a lower cost realization of arithmetic operations with a reduced hardware complexity. In addition, using the recursive multiplication technique, which refers to the published multiplication algorithm that uses smaller multipliers to implement a larger operation, reduces the size of operands and corresponding partial additions. As part of this research, 2DLNS-based multiplication architectures with two different levels of recursion are presented. These architectures combine some of the exibility of software with the high performance of hardware by implementing the recursive multiplication schemes on a 2DLNS processing structure. These implementations demonstrate the efciency of 2DLNS in DSP applications and show outvistanding results in terms of operation delay and dynamic power consumption. We also demonstrate the application of recursive 2DLNS multipliers to reconfigurable multiplication architectures. These architectures are able to perform single and double precision multiplication, as well as fault tolerant and dual throughput single precision operations. Modern DSP processors, such as those used in hand-held devices, may find considerable benefit from these high-performance, low-power, and high-speed recongurable architectures. In the final part of this research work, recursive 2DLNS multiplication architectures have been applied to a FIR lter structure. These implementations show considerable improvement to their binary counterparts in terms of VLSI area and 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.040 |
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".