A Retrospective and Prospective View of Approximate Computing
Access the article by W. Liu, F. Lombardi, and M. Schulte in Proceedings of the IEEE, March 2020.
Historically, computing has been designed to be as accurate and precise as possible. However, many applications do not require high precision, and excess precision has a major cost in terms of power, speed, and area on chip. This has become particularly important in applications such as AI in edge systems, where minimizing power and excess hardware are critical.
The authors survey the field of approximate computing, broadly defined as the variety of techniques in both software and hardware that can reduce precision to an acceptable level, without significantly reducing performance. Looking to the future, they indicate that capabilities for approximate computing can be integrated with tools for circuit and system design, test and verification, reliability, and security.
A future special issue of Proceedings of the IEEE with contributions on Approximate Computing is in preparation for later in 2020.
Accelerators for AI and HPC
Dr. Dejan Milojicic of Hewlett Packard Labs recently led a Virtual Roundtable Discussion on the present and future of accelerator chips for artificial intelligence (AI) and high-performance computing (HPC), which appeared in the February 2020 issue of Computer. The other participants were Paolo Faraboschi, Satoshi Matsuoki, and Avi Mendelson.
The central problem is how to deal with increasing complexity of heterogeneous hardware (CPUs, GPUs, FPGAs, ASICs, and multiple levels of memory) together with software that can efficiently use all of these resources to solve difficult computational problems. This is in addition to possible integration with new types of processors such as neuromorphic and quantum, which may become available in the next decade. All the participants agreed that continued improvements in performance will continue for the foreseeable future, both in small-scale (mobile) and large-scale (data center) computing, with continuing challenges along the way.
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.
Grand Challenge: Applying Artificial Intelligence and Machine Learning to Cybersecurity
Access the article by Kirk Bresniker, Ada Gavrilovska, James Holt, Dejan Milojicic, and Trung Tran in IEEE Xplore.
Providing future cybersecurity will require integration of AI/ML throughout the network worldwide. Initiating a series of Grand Challenges on this topic will help the community achieve this goal.
The December issue of Computer has a set of open-access feature articles on Technology Predictions. One of these is by Bresniker et al., on how AI/ML can help to address the pervasive and growing problem of cyberattacks. This follows a set of earlier workshops and a 2018 report (PDF, 1 MB) on a similar topic by some of the same authors.
The authors argue that given the massive scale of the problem, that it is continuously changing, and that rapid responses are needed, this can only be handled by a system of ubiquitous AI agents capable of machine learning. However, these autonomous AI agents must quickly incorporate the insights of the best human cyber analysts, many of whom work privately on non-public data sets. The authors propose that an annual Grand Challenge, with prizes as motivation, can help to bring about the necessary collaborations and competition to achieve this goal. Given the critical nature of the problem to business and government, this should be initiated as soon as possible.