Efficient Design Space Exploration of OpenCL Kernels for FPGA Targets Using Black Box Optimization
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
Nowadays, many industries are in favor of using intelligent design-space exploration as opposed to brute-force analysis. In many applications, the design-space is defined by multiple variables and their interactions. Although brute-force analysis is very simple, it is rarely scalable when the number of variables in the system increases. With the rising complexity of hardware designs, more intelligent approaches are needed to explore the design options. This paper proposes using smart meta-heuristic search algorithms such as Grey Wolf Optimization (GWO) in conjunction with Bayesian Optimization (BO) to solve this problem. We show that we can further reduce the design effort using a surrogate model that is created based on a novel hybrid GWO-BO method. The surrogate model is a useful abstraction to detect functional and physical inter-dependencies in the system in order to accurately predict its performance (e.g. throughput or latency). We evaluate our methodology and show that it can produce competitive results in order to find the best design variables that maximize performance of the system. Finally, we compare our results with previous statistical and heuristic methods proposed in the literature and find that the proposed GWO-BO method always performs better than the other considered methods.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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