University Produces Smart Compliers to Assist Research During ‘Dark Silicon’ Era

Researchers from Lancaster University believe a ‘dark silicon’ era is rapidly approaching. This era suggests more than 80 percent of computer processors’ transistors will be turned off and ‘remain dark’ in order to prevent the chips from overheating.

“Hardware design is rapidly evolving to prevent this need to ‘power down’ transistors and coming up with innovative solutions. But these improvements at hardware level bring with them complexities which are tricky for compilers to contend with. Unless we can find ways of helping compilers keep pace with these hardware changes they will no longer be able to efficiently translate high-level programming language or source code used by software into the machine code that computer hardware understands. Until this problem is solved, the software industry will stagnate; software will no longer be able to communicate efficiently with hardware and efforts to resolve the dark silicone problem will have been in vain,” according to Phys.org.

With assistance from a grant, the researchers are working toward finding solutions to the dark silicone era. Currently, the team is looking at new smart compliers which use machine learning to self-educate. The compliers also find more efficient ways of conducting the jobs between software and hardware.

“Software developers are struggling to cope with this dramatic increase in hardware complexity and the current tools are simply inadequate to the task. If we are unable to solve these problems then for the first time in decades, progress in the software industries will stagnate. Our project aims to provide enabling techniques at compiler-level using machine learning,” Zheng Wang, lecturer at Lancaster University, said.

“For the first time, machine learning will live in the application environment, learning how to optimize programs for individual computing devices. Our smart compilation system will acquire knowledge each time a program is compiled and run, and use the knowledge to learn how to optimize programs for each hardware platform and for each user. The more our system learns, the more it knows what works. Over time, programs will run faster and the entire computing system will become more energy efficient,” Zheng added.

Original Source http://ift.tt/1Fw5W6i

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