ActNAS: Generating Efficient YOLO Models Using Activation NAS
Bibliographic record
Abstract
Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns. Different activation functions impact differently on speed and accuracy; for instance, ReLU is fast but often less precise, while SiLU offers higher accuracy at the expense of speed. Traditionally, a single activation function is used throughout a model. In this work, we conducted a comprehensive study on the effects of using mixed activation functions in YOLO-based models, examining their impact on latency, memory usage, and accuracy across CPU, NPU, and GPU edge devices. We propose Activation NAS (Act-NAS)-a Hardware-Aware Neural Architecture Search (HANAS) method that optimizes activation functions per layer for specific hardware. ActNAS-generated models maintain comparable mean Average Precision (mAP) to baselines, while achieving up to 1.67 ⋉ faster inference and/or 64.15 % lower memory usage. Additionally, we demonstrate that hardware-aware models learn to leverage architectural and compiler-level optimizations, resulting in highly efficient performance tailored to each hardware platform.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".