Simplifying system-on-chip design through architecture and system cad tools
Bibliographic record
Abstract
Historically designers created computing systems by combining Integrated Circuits (ICs) on Printed Circuit Boards (PCBs), whereas now they are able to form complete Systems-on-Chip (SoCs). For the purpose of this study, SoCs are defined as a collection of functional units on one chip that interact to perform a desired operation. These modules are typically of a coarse granularity to promote reuse of previously designed Intellectual Property (IP). The decreasing size of process technologies enables designers to implement increasingly complex SoCs using both Application Specific Integrated Circuits (ASICs) and Field Programmable Gate Arrays (FPGAs). The impact of increasing design complexity is increased design time and costs for electronics. Therefore, this research investigates methods to facilitate the design of SoCs through both architecture and CAD tools. This thesis has two main contributions. The first is an architectural framework for SoCs, wherein they are modelled as Systems Integrating Modules with Predefined Physical Links (SIMPPL). The strength of the model is the Computing Element (CE) abstraction that separates the module's datapath from system-level control and communications to facilitate design reuse. Although SIMPPL can be used to build SoCs for ASICs or FPGAs, using an FPGA provides designers with a reprogrammable implementation platform. Thus, our second contribution is to develop a design infrastructure that leverages the advantages of reconfigurability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".