Convolutional Neural Network Hardware Optimization Using Bayesian Method
Bibliographic record
Abstract
Convolutional Neural Network (CNN) models have demonstrated significant benefits in the realm of computer vision and applications related to image processing. Optimizing hyperparameters in CNN models is crucial to ensuring an effective implementation of the model, whether on software, hardware, or a ‘software-hardware co-design’ platform, thereby enhancing overall performance and results. This work proposes a CNN architecture and applies the Bayesian optimization algorithm to find the best set of hyperparameter values which reduces training and recognition time both. In addition, a new parameter i.e., ‘Network optimization parameter’ (NOP) is defined which considers optimization of hardware resources for a given accuracy of the trained model. This parameter needs to be minimized which helps evaluate the best set of hyperparameter values and is essential for further implementing the CNN model in the hardware platform. The optimization is performed on both the processors, a Central Processing Unit (CPU) and a Graphical Processing Unit (GPU), in optimizing the CNN model to clearly understand the impacts of utilizing different processing units. An accuracy of 99.48 % is achieved for the Modified National Institute of Standards and Technology (MNIST) database, and an accuracy of 88.78 % is achieved for the Canadian Institute For Advanced Research (CIFAR-10) database. The proposed models are highly optimized and have lesser resource requirements (due to the lesser layer complexities and smaller filter sizes) while delivering higher accuracies compared to the available literature. Further, the calculated NOP for the proposed network is highly reduced compared to the published literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".