High-Bandwidth IJTAG over SSN
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
As Systems-on-Chip (SOC) designs grow in complexity, so do the challenges associated with testing them. Some of the obstacles SOC designers face include limited I/O and scan channels, routing and timing closure issues, increasing manufacturing test and defect diagnosis time, and growing test data volume. Various design-for-test (DFT) techniques exist to handle complex SOC designs that have multiple cores. One new DFT implementation technique is the streaming scan network (SSN) high-bandwidth parallel data bus. SSN addresses many of the SOC challenges by providing an optimized packet-based scan data delivery system. It also dynamically optimizes test time by adjusting the data applied to each core. However, SSN is limited to delivering scan data; it cannot be used to deliver data to individual instruments in a physical block using the IEEE 1687 (IJTAG) network. This paper introduces a new high-bandwidth IJTAG DFT technology that leverages the existing high-speed parallel SSN bus to drive the serial IJTAG network. It describes the DFT implementation methodology, the impact to the backend in terms of timing and SDC, and how verification was done by Intel as they deployed it on multiple dielets in their next generation client CPU. Moreover, data on area overhead and the overall test cost savings achieved is presented.
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 | 0.001 |
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 it