Exploring automated space/time tradeoffs for OpenVX compute graphs
Bibliographic record
Abstract
With the rise of FPGA-based implementation of Computer Vision (CV) applications, the need for a programming method that achieves the target throughput or area-budget while retaining flexibility is magnified. High-level synthesis (HLS) tools provide this opportunity while eliminating the necessity of hardware engineering knowledge. Existing methods of programming FPGAs with HLS require the user to explicitly manage resources at every stage in their algorithm in order to meet a specified area target or throughput target. In this paper, we provide a framework for meeting such targets with compute graphs specified in OpenVX, a C-based programming environment for computer vision. To do this, we build our own OpenVX system using Xilinx Vivado HLS, and add an algorithmic layer which allows the user to specify an area budget (while maximizing throughput) or a throughput target (while minimizing area). Our OpenVX system consists of a series of compute kernels, prewritten in C++ for HLS and heavily parameterized as well as an Intra-node Optimizer to enable the creation of different size/throughput targets using different image tile-sizes. It also uses a heuristic algorithm with an Inter-node Optimizer step which combines/splits kernels and then replicates them to minimize the area cost. We evaluate the system on typical OpenVX benchmarks under a variety of fixed area constraints, and find that our system is able to automatically achieve over 95% area utilization. We also evaluate the system with same benchmarks under variety of fixed throughput targets, and find our system saves up to 30% in area cost compared to manually parallelized implementations. Our results show our heuristic approach is able to hit the same throughput targets and save 19% area on average compared to existing ILP approaches. While existing methods can easily achieve a single design point, they are unable to automatically generate a set of solutions from the same source; a prominent capability embedded in our tool. Moreover our tool uses Inter-node Optimizer to find better space/time tradeoffs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".