Architecture tradeoffs in in reduced instruction set computers: a case study
Bibliographic record
Abstract
A major problem facing computer architects is the development of methods and techniques that measure and predict the performance of their architectures. This problem arises also in designing reduced instruction set computers (RISCs). The paper studies the effect of machine instruction set on performance of a subset of RISC architectures. In particular, the study investigates the behavior and performance issues of a minimal VLSI-estate RISC architecture (LDS RISC) designed by the Lambda Digital Synthesis Group in the Department of Computational Science, University of Saskatchewan. The authors address issues in architectural design. Such as register-to-memory architecture versus load/store, instruction frequency distribution, and suitability for pipelining as they apply to the LDS architecture. An instruction simulator and profiler are written for the LDS architecture. Performance statistics are gathered during the run-time of a benchmark suite on the LDS architecture. The authors anticipate that the gathered measurements serve as the knowledge base that can be used by an architect in making detailed design tradeoffs to achieve a better architecture. The proposed modifications aim for less memory references, and faster speed than the original LDS architecture.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".