Optimizing Co-Design of Hardware and ML Models

This chapter explores the integration of co-design principles in hardware and machine learning models, emphasizing the simultaneous optimization of both for enhanced performance. It discusses methodologies for characterizing neural networks concerning power, latency, and energy, while also introducing innovative approaches for architecture search that prioritize hardware constraints. The chapter highlights the importance of efficient architecture exploration and the application of model compression techniques to achieve energy efficiency without sacrificing accuracy.