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Feature Article

Feature Article

In-Memory Computing Challenges Come Into Focus

Researchers digging into ways around the von Neumann bottleneck.

Semiconductor Engineering online has a feature article on In-Memory Computing, available here.

This discusses a variety of developing memory technologies and applications that harness logic within the memory itself, rather than shuttling back and forth to a CPU. Redistribution of data has become the major bottleneck in performance in conventional von Neumann architectures. One class of in-memory computing consists of neural networks for pattern recognition, which have received great attention recently, and device technologies that can implement neural networks efficiently are being examined.

The article discusses research into new devices and architectures at HP, IBM, IMEC, Stanford, Berkeley, Michigan, Minnesota, and Tsinghua Universities. Both digital and analog solutions are being examined. Memory technologies include resistive RAMs (RRAMs), electrochemical RAMS (ECRAMs), and flash memories.

It is not yet clear which devices will be incorporated into next-generation computing systems, but widespread future demand for data analysis using neural network and other processors will be present from IoT and mobile devices all the way to data centers.

Technology Spotlight

Technology Spotlight

Reversible Computing for Energy Efficiency

At the recent IEEE International Conference on Rebooting Computing, held in Washington DC in November as part of IEEE Rebooting Computing Week, one of the invited talks was by Dr. Michael Frank of Sandia National Laboratory.

Dr. Frank spoke about “Reversible Computing as a Path Towards Unbounded Energy Efficiency”. The video of Dr. Frank’s talk is available here.

Reversible computing is an alternative paradigm for computing, whereby intermediate data are not discarded or overwritten during a computation, but instead are saved. It has long been known that reversible computing offers the possibility of several orders of magnitude reduction in energy dissipation, but little attention was paid while Moore’s Law was active. Now that Moore’s Law is ending, this novel approach deserves a second look. This will require development of new devices, circuits, systems, and algorithms, but current research suggests that major improvements are possible with fairly modest investments in R&D. Some of this research has used low-dissipation technologies such as superconducting devices, but great improvements are possible even using more conventional CMOS devices.

An earlier introductory article on Reversible Computing by Dr. Frank was presented in IEEE Spectrum in 2017 here.

Videos of other ICRC 2018 talks are available from IEEE.tv.

The Proceedings of ICRC 2018 is available from IEEE Xplore here.