Bibliographic record
Abstract
A generic model for test architectures in the core-based system-on-chip (SoC) designs consists of source/sink, wrapper, and test access mechanism (TAM). Current test architectures for digital cores assume a direct connection between the core and the tester. In these architectures, the tester establishes a physical link between itself and the core, such that it can directly control the core's design-for-testability (DFT), such as the scan chains or primary inputs. This direct connection undermines the modularity in the generic test architecture by tightly coupling its elements. In this paper, we propose a network-oriented indirect and modular architecture (NIMA) for postfabrication test in an SoC design methodology. In NIMA, test stimuli and expected results for digital cores are first compiled into new formats and subsequently encapsulated into packets. These packets are augmented with control and address bits such that they can autonomously be transmitted to their destination through a switching fabric. Owing to the indirect nature of the connection, embedded autonomous blocks at each core are used to apply the test to the core and compare the test results with expected values. This indirect access to the core decouples test data processing at the core from its communication providing the basis for flexible and modular test design and programming. Moreover, NIMA facilitates remote-access of single or multiple testers to an SoC, and enables the sending of test data to an SoC in-field in order to test the chip in its target system. Finally, NIMA serves in contributing toward the development of new test architectures that benefit from network-centric SoCs. We present a first implementation of NIMA when applied to a number of SoC benchmarks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.001 |
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".