The discussion highlights the ongoing challenges in achieving a seamless co-design between hardware and machine learning models. Emphasizing the need for a comprehensive solution that accommodates multiple tasks and modalities, it explores how current approaches are limited to single models on specific hardware. The goal is to enable efficient execution across various applications, such as robotic vision and AR/VR, while considering the intricate interplay of hardware metrics and model requirements.
