Application-driven design of DSP architectures and compilers
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
Current DSP architectures are designed to enhance the execution of computationally-intensive, kernel-like loops. Their peculiar architectural features are often difficult for high-level language compilers to exploit. Moreover, their tightly-encoded instruction sets usually restrict the exploitation of instruction-level parallelism beyond a few instances. The quality of compiler-generated code is therefore poor when compared to hand-coded assembly language. We argue for an application-driven approach to designing flexible DSP architectures and effective compilers. We show that the run-time behavior and architectural characteristics of DSP kernels are different from those of DSP applications. We also show that when given a sufficiently flexible target architecture, a compiler is capable of effectively exploiting instances of instruction-level parallelism and DSP-specific architectural features. Finally, we show that a suitable DSP architecture is one that provides the functionality to support digital signal processing requirements, and the flexibility that enables a compiler to generate efficient code.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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) | 0.000 | 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