Hardware Constraint

Hardware constraints in machine learning, particularly for deploying complex models like large language models (LLMs) on resource-limited devices, are a major research focus. Current efforts concentrate on optimizing model architectures through neural architecture search (NAS) and developing efficient hardware accelerators, often leveraging FPGAs for their flexibility, alongside co-optimization of hardware and software components to minimize memory access and latency. This research is crucial for enabling the widespread deployment of AI in edge computing and Internet of Things (IoT) applications, impacting fields ranging from medical devices to autonomous systems.