A COMPUTATIONAL-RAM (C-RAM) ARCHITECTURE FOR REAL-TIME MESH-BASED VIDEO MOTION TRACKING PART 2: MOTION COMPENSATION
Bibliographic record
Abstract
This paper presents a new Computational-RAM (C-RAM) architecture for real-time mesh-based video motion tracking. In Part 1, the motion estimation part of the proposed architecture is presented. Here in Part 2, a new C-RAM mesh-based motion compensation architecture is presented. The input data to the architecture is the mesh nodes motion vectors and the reference frame and the output data is the compensated (i.e., predicted) frame. The architecture uses the affine transformation for warping the deformed patches in the reference frame into the undeformed patches in the current frame. The architecture computes the affine parameters using a multiplication-free algorithm. The reference and current frames are stored in embedded S-RAMs generated with Virage™ Memory Compiler. The proposed motion compensation architecture has been prototyped, simulated and synthesized using the TSMC 0.18 μm CMOS technology. Using 100 MHz clock frequency, the proposed architecture processes one CIF video frame (i.e., 352×288 pixels) in 0.59 ms, which means it can process up to 1694 frames per second. The core area of the proposed motion compensation architecture is 28.04 mm2 and it consumes 31.15 mW.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".