Bit-Serial Digital Filter Implementation using a Custom C Compiler
Bibliographic record
Abstract
Bit-serial arithmetic offers the potential for more compact designs and increased levels of functional parallelism in comparison to bit-parallel arithmetic. While these advantages come at the expense of decreased throughput, there are areas of digital signal processing where the trade-off is desirable. Unfortunately, designers often overlook bit-serial arithmetic, partly due to a lack of design tools. This paper describes the design and implementation of a compiler which generates bit-serial designs from a high-level language based on C. The compiler targets a synthesizable VHDL bit-serial library, relying on a conventional VHDL backend for placement and routing. To exploit the relative low hardware cost of bit-serial operations, the compiler employs techniques developed for conventional optimizing compilers to extract fine-grained parallelism from high-level algorithms. Working from a high-level description of an algorithm, a designer can generate different design implementations from a single version of the source, using a parameterizable system word length, or by specifying a compiler option to trade-off latency for reduced parallelism, and therefore reduced hardware cost
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".