SpaceCAM: A 16 nm FinFET Low-Power Soft-Error Tolerant TCAM Design for Space Communication Applications
Why this work is in the frame
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Bibliographic record
Abstract
The Ternary Content Addressable Memory (TCAM) is a crucial component of satellite communication systems. Space-oriented TCAMs face unique challenges, as they must operate within a very limited energy budget and are susceptible to high Soft Error Rates (SER) due to ionizing particle radiation. The Dual Interlocked Storage Cell (DICE) based memory is capable of withstanding soft errors. However, its reliability diminishes in presence of multiple node upsets. Moreover, recent studies indicate that DICE resilience to even single-node upsets degrades in advanced technology nodes. This issue is further exacerbated by the scaling of the supply voltage to reduce power consumption. In this paper, we propose SpaceCAM, a DICE-based TCAM that overcomes the above limitations and enables aggressive voltage scaling while withstanding multiple node upsets in each memory row. SpaceCAM enables soft error tolerance by applying an approximate rather than an exact search. It tolerates up to 5 soft errors per 144-bit row, provided the minimum Hamming distance between stored data patterns (such as the Active Control List (ACL) rules) is 26. When designed using a 16nm FinFET commercial process, SpaceCAM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$144\times 512$ </tex-math></inline-formula>-bit memory core operates at a supply voltage of as low as 350mV, consuming 2mW while running at 500MHz.
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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 | 0.001 | 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