A reformulated systematic resampling algorithm for particle filters and its parallel implementation in an application-specific instruction-set processor
Bibliographic record
Abstract
Particle filters (PFs) are computationally intensive, which prevents them from being widely used in some real-time applications with high throughput requirements. A parallel implementation is a feasible approach to enable using PFs in these applications. However, effective resampling algorithms such as the Systematic Resampling (SR) algorithm are sequential in nature. In this paper, we propose a new form of the SR algorithm suitable for parallel implementation in an Application-Specific Instruction-set Processor (ASIP). Six custom instructions were designed for this reformulated SR algorithm. Experimental results show that the ASIP implementation of the reformulated SR algorithm, with four weights calculated in parallel, and eight categories defined by uniformly distributed numbers that are compared simultaneously to achieve a 30.6× speedup over the serial SR algorithm in a general-purpose processor. This comes at a cost of only 54K additional gates, or 68% overhead to be added to a base processor with 79K gates.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".