- New project on DNA-based memory technology - Collaboration between Georgia Tech, Microsoft, University of Washington, and others.
- Princeton Quantum Initiative - New interdisciplinary program in quantum computing at Princeton University
- International Race to Demonstrate First Exascale Computer - China, US, EU, and Japan competing, with China’s entry possible in 2020.
- Neural Network Processor for Machine Learning - Intel announces new commercial AI chips for training and inference.
- Quantum Information Edge - New consortium on quantum computing led by US National Laboratories, with universities and industry.
- Update of National Strategic Computing Initiative - New Report from US Office of Science and Technology Policy addresses changes from original 2016 plan, including new emphasis on cybersecurity.
- Quantum Volume Explained - Metric promoted by IBM for comparing prototype quantum computing systems.
- Quantum Supremacy? - Google’s quantum tech milestone excites scientists and spurs rivals.
- Heterogeneous Integration Roadmap - Industry consortium SEMI announces new 15-year roadmap for electronic packaging and systems.
- Low-energy alternative to Bitcoin? - New financial algorithms are secure but much more energy-efficient than blockchains.
- Carbon Nanotube Microprocessor - 16-bit programmable processor based on 3D integrated circuit including 15,000 transistors.
- Moore’s Law for Circuit Density is Continuing - Smaller circuits and 3D integration may enable growth in density for 30 years.
- Artificial Intelligence Roadmap Released by Computing Community Consortium - Project key areas of collaborative research over next 20 years in academia, industry, and government.
Benchmarking Delay and Energy of Neural Inference Circuits
By Dmitri Nikonov and Ian Young, Intel
In recent years, a wide variety of device technologies have been developed to implement neural network algorithms, for artificial intelligence and machine learning (AI/ML). These have included both digital and analog CMOS circuits, but also different beyond-CMOS devices, such as a range of non-volatile memory arrays. In determining which of these approaches may be preferred for low-power applications, it is important to develop benchmarks that permit quantitative comparison.
The authors first evaluate neural switching on the device level, and compute the switching energy and delay for each technology, on the same series of plots. The results differ by orders of magnitude between different technologies, and even for different devices in similar technologies. They then perform similar computations for total energy and time delay for various prototype neural network chips to perform the same inference algorithm. Again, the results vary by large factors. Analog neural networks are found to be somewhat faster and lower power than digital circuits, for the same degree of precision. While this technology is still developing, this sort of analysis may be useful in evaluating the most promising approaches.
Highlights from the Industry Summit on the Future of Computing
Bruce Kraemer, IEEE Industry Summit Chairman
The IEEE Industry Summit on the Future of Computing was held in San Mateo, California, on 4 November 2019, and consisted of a series of invited talks and panel presentations by leaders in the field. The slides for many of the presentations are linked from the Summit Program, and videos for many of the presentations are available on IEEE.tv, together with other presentations from IEEE Rebooting Computing Week.
Summit Chairman Bruce Kraemer presented a brief overview of invited speakers on quantum computing, AI, memory-centric computing, and a panel on startups, with the video available on IEEE.tv.
In terms of all of these approaches to future computing, the technologies at these preliminary stages are remarkably diverse. For example, speakers on quantum computing presented superconducting, optical, and semiconducting solutions. Performance benchmarks that will permit these alternative technologies to be compared are still being developed. Despite the concerns of some that Moore’s Law is ending, there was agreement that this is an exciting time for the computer industry.
- Rebooting Computing Video Overview
- IEEE Future Directions
- 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
- 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
- RC-ITRS Report on the Foundation of the New Computer Industry Beyond 2020