High-Throughput 0.13-$\mu{\rm m}$ CMOS Lattice Reduction Core Supporting 880 Mb/s Detection
Bibliographic record
Abstract
This paper presents the first silicon-proven implementation of a lattice reduction (LR) algorithm, which achieves maximum likelihood diversity. The implementation is based on a novel hardware-optimized due to the Lenstra, Lenstra, and Lovász (LLL) algorithm, which significantly reduces its complexity by replacing all the computationally intensive LLL operations (multiplication, division, and square root) with low-complexity additions and comparisons. The proposed VLSI design utilizes a pipelined architecture that produces an LR-reduced matrix set every 40 cycles, which is a 60% reduction compared to current state-of-the-art LR field-programmable gate array implementations. The 0.13-μm CMOS LR core presented in this paper achieves a clock rate of 352 MHz, and thus is capable of sustaining a throughput of 880 Mb/s for 64-QAM multiple-input-multiple-output detection with superior performance while dissipating 59.4 mW at 1.32 V supply.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".