A COMPUTATIONAL-RAM (C-RAM) ARCHITECTURE FOR REAL-TIME MESH-BASED VIDEO MOTION TRACKING PART 2: MOTION COMPENSATION
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
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 mm 2 and it consumes 31.15 mW.
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 | 0.001 | 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