A reformulated systematic resampling algorithm for particle filters and its parallel implementation in an application-specific instruction-set processor
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
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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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 | 0.001 |
| 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