Moore's Law is slowing at a time when new applications are demanding more muscle. The solution is to offload jobs to specialized hardware but these complex, heterogeneous systems will require a fresh approach.

Chip designers face a daunting task. The tool that they have relied on to make things smaller, faster and cheaper, known as Moore's Law, is increasingly ineffective. At the same time, new applications such as deep learning are demanding more powerful and efficient hardware.

It is now clear that scaling general-purpose CPUs alone won't be sufficient to meet the performance per watt targets of future applications, and much of the heavy lifting is being offloaded to accelerators such as GPUs, FPGAs, DSPs and even custom ASICs such as Google's TPU. The catch is that these complex heterogeneous systems are difficult to design, manufacture and program. One of the key themes at the recent Linley Processor Conference was how the industry is responding to this challenge.

"Architects today are faced with an enormous, almost insurmountable problem," said Anush Mohandass, a marketing vice president at NetSpeed Systems. "You need CPUs, you need GPUs, you need vision processors, and all of these need to work together perfectly."

At the conference, NetSpeed --a private company that specializes in the scalable, coherent network-on-chip technology used to glue together the pieces of a heterogeneous processors --announced Turing, a machine learning algorithm that optimizes chip designs for processors targeted at automotive, cloud computing, mobile and the Internet of Things. Mohandass talked about the how the system often comes up with "non-intuitive recommendations" to meet the design goals not only for power, performance and area, but also the functional safety requirements that are essential in automotive and industrial sectors...

Read more: Chipmakers find new ways to move forward | ZDNet

Posted By: Brink
19 Oct 2017