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11 Ways To Reduce AI Energy Consumption


As the machine-learning industry evolves, the focus has expanded from merely solving the problem to solving the problem better. “Better” often has meant accuracy or speed, but as data-center energy budgets explode and machine learning moves to the edge, energy consumption has taken its place alongside accuracy and speed as a critical issue. There are a number of approaches to neural netw... » read more

Power Models For Machine Learning


AI and machine learning are being designed into just about everything, but the chip industry lacks sufficient tools to gauge how much power and energy an algorithm is using when it runs on a particular hardware platform. The missing information is a serious limiter for energy-sensitive devices. As the old maxim goes, you can't optimize what you can't measure. Today, the focus is on functiona... » read more

Searching For Power Bugs


How much power is your design meant to consume while performing a particular function? For many designs, getting this right may separate success from failure, but knowing that right number is not as easy as it sounds. Significant gaps remain between what power analysis may predict and what silicon consumes. As fast as known gaps are closed, new challenges and demands are being placed on the ... » read more

One-On-One: Aart de Geus


Synopsys' CEO talks with Low-Power Engineering about the future of EDA, the changes in IP, stacked die and 20nm designs. [youtube vid=x9TKRC48OG0] » read more