Embedded ISA support for enhanced floating-point to fixed-point ANSI-C compilation
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
Recently tools for automating the translation of floatingpoint signal-processing applications written in ANSI C into fixed-point have been presented [34, 17, 8]. This paper introduces a novel fixed-point instruction-set operation, Fractional Multiplication with internal Left Shift (FMLS), and an associated translation algorithm—Intermediate-Result-Profiling based Shift Absorption (IRP-SA), that enhance fixedpoint rounding-noise and runtime performance. A significant feature of FMLS is that it is well suited to the latest generation of embedded processors that maintain relatively homogeneous register architectures. FMLS may improve the rounding-noise performance of fractional multiplication operations in three ways depending upon the specific fixed-point scaling properties an application exhibits. The IRP-SA algorithm enhances this by exploiting the modular nature of 2’s-complement addition which allows the discarding of most-significant-bits that are redundant due to inter-operand correlations. Rounding-noise reductions equivalent to carrying as much as 2.0 additional bits of precision throughout the computation are presented. Furthermore, by encoding a very limited set of output shift values (two left, one left, none, and one right) into the FMLS operation, speedups of up to 13 percent are observed. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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