• IBM reports improved performance of high-speed, high-density memories - Spin-transfer-torque MRAM embedded in 14 nm circuits demonstrated with ns switching.
• 3D integration of heterogeneous chips for exascale processors - CEA (France) develops chiplet-level packaging with increased bandwidth and density.
• Graphene-Based Memristors for Improved Neural Networks - Penn State researchers demonstrate synapses with multiple synaptic weights
• Intel develops new cryptography protocols - Designed to be resistant to hacking by future quantum computers
• New memristor device acts like biological neuron - May provide basic element for neuromorphic computing
• Record speeds for AI inferencing - Nvidia GPU exceeds prior benchmark performance
• Analog Optical Computing Chip for Neural Networks - Carries out multiply-accumulate computations in silicon photonic chip for AI.
• US Government Announces New Research Centers in AI and QC - $1B in government labs and universities funded by National Science Foundation and Dept. of Energy.
• Newsletter of IEEE Electron Devices Society features new IRDS™ Roadmap - Provides history of earlier semiconductor roadmaps and an overview of the latest roadmap.
• US Government funds 3 academic-based institutes on quantum information - $75M over five years going to research based at the University of Colorado, University of California at Berkeley, and the University of Illinois.
• Chip Proposals Seek to Revive US Manufacturing - US Congress proposes aid to advance US semiconductor industry.

Exploratory Devices and Circuits for Compute-in-Memory

Special Issue of IEEE Journal of Exploratory Solid-State Computational Devices and Circuits, June 2020

The IEEE Journal of Exploratory Solid-State Computational Devices and Circuits (JXCDC) is an open-access journal which publishes multi-disciplinary research in solid-state circuits using exploratory materials and devices for novel energy efficient computation beyond standard CMOS.

The June special issue is on Compute-in-Memory (CIM), with 10 papers selected by special editor Prof. Shimeng Yu of Georgia Tech.

Deep learning in neural networks has become one of the major applications of recent computing, in both cloud and edge systems. These networks require large matrix multiplications, with arrays of multiply-and-accumulate (MAC) circuits. These can be carried out conventionally in the digital mode, using CPUs or more efficiently with GPUs. A developing alternative carries out the matrix multiplication in the analog domain, in memory arrays. The input can be sent in on rows, and the output read out on columns, where the matrix multiplication follows naturally from the conversion of voltage to current. Of course, this requires the use of digital-to-analog converters (DACs) on the inputs and analog-to-digital converters (ADCs) on the output.

A number of alternative device technologies are being evaluated for CIM, mostly non-volatile memory arrays. These include memristors and resistive RAM (RRAM), spintronic memories, and phase-change memories. The articles in the special issue evaluate a number of these alternatives, in terms of power, density, scalability, and integration with CMOS digital processing.

The overview of the special issue and the index of articles, all of which are open access, are available at IEEE Xplore.

In Pursuit of 1000X: Disruptive Research for the Next Decade of Computing

Keynote Video Lecture for Intel Labs Day, 3 December 2020

Coordinated by Dr. Richard Uhlig, Director of Intel Labs

In the past, the semiconductor industry depended on Moore’s Law to achieve exponential improvement in computing performance. But Moore’s Law is approaching its limits, so looking to the future, alternative approaches are needed to maintain growth. Intel Labs is carrying out advanced research into a variety of novel approaches in hardware and software that promise to enable improvements of 1000X or more in terms of speed, efficiency, or other performance metrics. Dr. Uhlig introduces Intel researchers who address the 5 following approaches:
(1) Integrated Photonics
(2) Neuromorphic Computing
(3) Quantum Computing
(4) Confidential Computing
(5) Machine Programming

An overview of the lecture is available at HPCwire.

The 55-minute keynote video and the complete slide deck (PDF, 9 MB) are also available.

The entire set of videos from Intel Labs Day is available at the Intel website.

• NEW: 2020 CCC Workshop on Physics and Engineering Issues in Reversible/Adiabatic Classical Computing
• Rebooting Computing Video Overview
• IEEE Future Directions
• IEEE Future Directions Blog
• Computing in Science and Engineering on the End of Moore's Law
• IEEE Journal of Exploratory Solid-State Computational Devices and Circuits (JXCDC)
• Arch2030 Workshop Report (PDF, 948 KB)
• Workshop on Neuromorphic Computing
• Workshop on Beyond CMOS Technology
• Update on National Strategic Computing Initiative (NSCI)
• RC White Paper on Nanocomputers
• IEEE Computer Magazine on Rebooting Computing