Machine Learning Offers Helping Hand To Edit Chips

Tasked with squeezing billions of transistors onto fingernail-sized slabs of silicon, chip designers are asking whether machine learning can help. In the view of electronic design automation firms, machine learning tools could chisel rough edges off complex chips, improving productivity, optimizing trade-offs like power consumption and timing, and testing that chips are ready for manufacturing. Though chip design is still a creative process, engineers need tools that abstract the massive number of variables in modern chips. Using statistics, the software generates models fitted to simulations that replicate how physical chips will work. The tools would seem to be prime candidates for machine learning, which can be trained to find hidden insights in data without explicit programming.