The Good, the Bad and the Ugly: Practices and Perspectives on Hardware Acceleration for Embedded Image Processing
Bibliographic record
Abstract
Abstract Modern embedded image processing deployment systems are heterogeneous combinations of general-purpose and specialized processors, custom ASIC accelerators and bespoke hardware accelerators. This paper offers a primer on hardware acceleration of image processing, focusing on embedded, real-time applications. We then survey the landscape of High Level Synthesis technologies that are amenable to the domain, and present our ongoing work on IMP-Lang, a language for early stage design of heterogeneous image processing systems. We show that hardware acceleration is not just a process of converting a piece of computation into an equivalent hardware system: that naive approach offers, in most cases, little benefit. Instead, acceleration must take into account how data is streamed throughout the system, and optimize that streaming accordingly. We show that the choice of tooling plays an important role in the results of acceleration. Different tools, in function of the underlying language paradigm, produce wildly different results across performance, size, and power consumption metrics. Finally, we show that bringing heterogeneous considerations to the language level offers significant advantages to early design estimation, allowing designers to partition their algorithms more efficiently, iterating towards a convergent design that can then be implemented across heterogeneous elements accordingly.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".