Scalable VLSI architecture for K-best lattice decoders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A scalable pipelined VLSI architecture for K-best lattice decoders featuring an efficient operation over infinite lattices is proposed. The proposed architecture operates at a significantly lower complexity than currently reported schemes. The key contribution is a means of expanding/visiting the intermediate nodes of the search tree on-demand, rather than exhaustively along with three types of distributed sorters operating in a pipelined structure. The combined expansion and sorting cores are able to find the K best candidates in just K clock cycles. Its support of the unbounded lattice decoding distinguishes our work from previous K-best strategies. Since the expansion and sorting cores cooperate on a data-driven basis, the architecture is well-suited for a pipelined parallel VLSI implementation. The proposed architecture has the lowest latency reported to-date, fixed critical path independent of the constellation order, on-demand expansion scheme, efficient distribute sorters, pipelined high-throughput implementation, and is scalable to higher number of antennas/constellation orders.
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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) | 0.000 | 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) | 0.000 | 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 it