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Designing special-purpose hardware should be as easy as writing software

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December 14, 2016

The International Symposium on Computing Architecture has revealed the 5 architectural challenges it estimates that computer science needs to solve to fully meet the industry's demands in fourteen years from now.

Their various recommendations, distilled from the Architecture 2030 Workshop at June's ISCA in Korea, draws on the contributions of speakers from several universities, the IEEE's Rebooting Computing Initiative & International Roadmap of Devices and Systems.

The resulting document, dubbed ``Arch-2030: A Vision of Computer Architecture Research over the Next Fifteen Years`` starts by saying that we currently have a specialization gap.

Overall, computing technology has improved immensely in recent decades, the authors assert, simply because we've coasted on Moore's Law. To keep up with the demands of future workloads, “Developing hardware must become as easy, simple, inexpensive and agile as writing software.”

Next comes a call for “The Cloud as an Abstraction for Architecture Innovation”. Translated, this means researchers should go to town using all of cloud providers' best bits – machine-learning optimized CPUs, FPGAs, GPUs in large numbers, to create otherwise unimaginable architectures.

The authors also assert that researchers must redouble their efforts to virtualize those architectures so they can span different clouds.

For example, 3D integration in silicon, “shortening interconnects by routing in three dimensions, and facilitating the tight integration of heterogeneous manufacturing technologies” is also recommended.

If it can be pulled off, we'll get “greater energy efficiency, higher bandwidth, and lower latency between system components inside the 3D structure.”

As does a call for architectures “Closer to Physics”, a phrase used to call for devices that make use of new materials, or techniques like quantum computing, that emphasise analog processing of data instead of today's approach of forcing digital computing into manufactured tools.

Processors assembled from carbon nanotubes “promise greater density and lower power and can also be used in 3D substrates,” the document suggests.

Lastly, the ISCA identifies ML (machine learning) as 2030's most in-demand workload and offers the following observation about how to deliver it-- “While the current focus is on supporting ML in the Cloud, significant opportunities exist to support ML applications in low-power devices, such as smartphones or ultralow power sensor nodes.”

“Luckily, many ML kernels have relatively regular structures and are amenable to accuracy-resource trade-offs. Hence, they lend themselves to hardware specialization, reconfiguration, and approximation techniques, opening up a significant segment for architectural innovation,” the scientists assert.

Source: The International Symposium on Computing Architecture.


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