Effectiveness of a Reinforcement-Learning Based Dynamic Power Manager In a SW Framework


New technical paper titled "Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM" from researchers at ETH Zurich and Georgia Tech. Abstract "With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiven... » read more

Tricky Tradeoffs For LPDDR5


LPDDR5 is slated as the next-gen memory for AI technology, autonomous driving, 5G networks, advanced displays, and leading-edge camera applications, and it is expected to compete with GDDR6 for these applications. But like all next-gen applications, balancing power, performance, and area concerns against new technology options is not straightforward. These are interesting times in the memory... » read more

Optimizing Power For Learning At The Edge


Learning on the edge is seen as one of the Holy Grails of machine learning, but today even the cloud is struggling to get computation done using reasonable amounts of power. Power is the great enabler—or limiter—of the technology, and the industry is beginning to respond. "Power is like an inverse pyramid problem," says Johannes Stahl, senior director of product marketing at Synopsys. "T... » read more

More Problems Ahead


Semiconductor Engineering sat down to discuss future scaling problems with Lars Liebmann, a fellow at IBM; Adam Brand, managing director of transistor technology at Applied Materials; Karim Arabi, vice president of engineering at Qualcomm; and Srinivas Banna, a fellow for advanced technology architecture at GlobalFoundries. SE: We’re starting to hear talk about octuple patterning. We’ve ... » read more