About this document
Machine Learning for Hardware Approximation by smanasvitareddyp is a document available to read on EtoBox.
The document presents a tutorial on hardware approximation techniques driven by machine learning models, aimed at reducing power consumption in electronic circuits. It discusses the application of machine learning in designing approximate components and synthesizing hardware accelerators, highlighting various methodologies and their benefits. The tutorial also emphasizes the importance of automated design systems and the role of machine learning in enhancing the efficiency of circuit approximation processes
- Author
- smanasvitareddyp
- Language
- EN