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Feature Article

Feature Article

Benchmarking Delay and Energy of Neural Inference Circuits

Access the article in IEEE Xplore

By Dmitri Nikonov and Ian Young, Intel

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits

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.

Technology Spotlight

Technology Spotlight

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.