Assessing the Performance of an Architecture-Aware Optimization Tool for Neural Networks
Resumo
The important growth in the demand for Neural Network solutions has created an urgent need for efficient implementations across a wide array of environments and platforms. As industries increasingly rely on AI-driven technologies, optimizing the performance and effectiveness of these networks has become crucial. While numerous studies have achieved promising results in this field, the process of fine-tuning and identifying optimal architectures for specific problem domains remains a complex and resource-intensive task. As such, there is a pressing need to explore and evaluate techniques that can improve this optimization process, reducing costs and time-to-deployment while maximizing the overall performance of Neural Networks. This work focuses on evaluating the optimization process of NetAdpat for two neural networks on an Nvidia Jetson device. We observe a performance decay for the larger network when the algorithm tries to meet the latency constraint. Furthermore, we propose potential alternatives to optimize this tool. Particularly, we propose an alternative configuration search procedure that allows us to enhance the optimization process, achieving speedups of up to ∼7/×.
