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.
An Outlook for Quantum Computing
Proceedings of the IEEE recently published an overview of the present and future status of quantum computing, by Dmitri Maslov, Yunseong Nam, and Yungsang Kim, of the US National Science Foundation and IonQ, Inc.
This work presents a qubit technology associated with trapped ions coupled by optical pulses. While this technology is different from the superconducting integrated circuit approach being pursued by other projects, it has advantages in not requiring deep cryogenic temperatures for operation, and also offers long coherent times. The ion traps can be microfabricated on a chip, as shown in the figure.
Current quantum computing technologies are noisy intermediate-scale quantum systems (NISQ), which cannot carry out desired quantum algorithms without quantum error correction, which is not yet available. The next major step is to demonstrate that a quantum computer can be used to solve a problem of practical utility that cannot otherwise be addressed, such as various kinds of quantum simulations. The transition of the proof-of-concept devices to useful computational systems faces a set of new technical challenges, ranging from improving and expanding qubit hardware to developing control/operating systems to innovations in algorithms and applications.
This issue of Proceedings of the IEEE also contains a set of other articles on alternative modes of computing. See here for the Table of Contents.
Artificial Synapses for AI
IEEE Spectrum describes recent progress in the development of nanoscale memory cells that may be applied to variable artificial synapses for artificial neural networks, reported here.
This describes work at IBM Research on an electrochemical random-access memory cell, or ECRAM, where a gate drives lithium ions into or out of a tungsten trioxide channel, changing the channel resistance. What is required for neural network applications is a precise change in resistance, depending on the drive voltage, which is rapid and repeatedly reversible. This was presented at the International Electron Device Meeting in San Francisco in December. Other related work reported at IEDM included novel ferroelectric FETs (FeFET) from Purdue University, University of Notre Dame, and Samsung, which may also be applied to chips for neural networks.