A multi-mode video-stream processor with cyclically reconfigurable architecture
Bibliographic record
Abstract
This paper presents an approach for development of cost-effective hardware platform for video/image processing. The approach utilizes the SRAM based reconfigurable logic devices (FPGAs) and, their capability of run-time temporal partitioning of logic resources. We propose the architecture for multi-mode video-stream processor with cyclically reconfigurable structure. The proposed architecture has been analyzed on the basis of experiments conducted on AMIRIX AP1000 development system based on Xilinx Virtex-2Pro FPGA. Multi-mode Adaptive Reconfigurable System has been developed, based on Xilinx Virtex-4 FPGA. This platform is capable of supporting the runtime temporal partitioning of on-chip resources. The main component of the research was the introduction of methodology of design for cyclically reconfigurable processor that uses the temporal partitioning mechanism (TPM). TPM allows reuse of the logic and routing resources of an SRAM based FPGA device by the means of partitioning algorithm in to tasks and execution of these tasks in different time slots. This technique allows the reduction of size requirement for FPGA devices, as well as, increase in cost efficiency, and decrease in power consumption of the system compared to systems with statically configured FPGA devices. Applications associated with stereo-vision algorithms and object tracking have been developed and tested on the platform. Finally, the analysis of the cost-effectiveness of this approach has been conducted. This analysis has demonstrated sufficient increase of efficiency in comparison to statically configured FPGA designs. Work also presents optimal conditions at which the use of the architecture would be most cost effective, and where the use of it would be most beneficial. The experimental tests have been done by the means of development of application that are used in the industry in the area of stereo-vision space-born applications.
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.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".