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Computing: Analog’s Chance to One-Up Digital
Analog computers have long been considered no match to digital ones. Nearly fifty years ago, Theodore Nelson, the inventor of hypertext, wrote that “there are two kinds of computers: analog and ditigal. Analog computers are so unimportant compared to ditial computers that we will polish them off in a couple of paragraphs.”
But now, while it may be an uphill battle to supplant digital, analog computers are being recognized as useful in applications like neural systema, the modeling of biological systems, and quantum computing. Analog computers are being built from living tissues and with materials like graphene.
Hiroyuki Nagashima, U.S. general manager at Alchip, said that “the digital AI ASIC might not be the ideal solution for IoT edge computing due to its high-power consumption and form factor. Mixed-signal machine learning, inspired by nature like the human brain, should play an import role in the future world.”
Youbok Lee, senior technical staff engineer at Microchip Technology, said that “in deep learning AI applications, we see various research reports that some forms of machine learning tasks do not require high precision. Better energy efficiency and faster speeds are more important than accuracy. For example, 1-bit processing can still obtain high accuracy. Because of this, analog techniques, which can be more energy-efficient than digital at low precision, can be used as a co-processor to accelerate workloads that are traditionally performed on digital but with up-to-5x better energy efficiencies.”
Tim Vehling, senior vice president at Mythic, wrote that “times have changed and when we look at the needs of the future—one where every device will be equipped with powerful AI at the edge—it’s clear that digital compute won’t be able to keep up. With analog compute, algorithms typically requiring a large, power-hungry GPU will be run on a small, low-power, cost-effective chip that can be integrated into any device.”
