NIST Researcher Contends Optoelectronics Is Best Path to General AI

By John Russell

May 26, 2021

Discussion around how to create, or whether to create, or if it is even possible to create general artificial intelligence has simmered for years. Sticking to the how-to element, a wide variety of schemes and technologies have been and are being explored. Recently a paper from a prominent researcher at the National Institute of Standards and Technology suggests that an optoelectronic strategy is the most likely approach to succeed in creating general AI.

Jeffrey Shainline, a scientist in the quantum nanophotonics group at NIST, argues in a perspective published last month in Applied Physics Letters, “It is the perspective of our group at NIST that hardware incorporating light for communication between electronic computational elements combined in an architecture of networked optoelectronic spiking neurons may provide potential for AGI at the scale of the human brain.”

General AI – sometimes called strong AI, full AI, or general intelligent action – is broadly used for the idea of a machine possessing sentience, self-awareness, and consciousness. Weak or narrow AI is typically used to describe more limited capabilities. (Today’s world, of course, is awash in technical and marketing buzz phrases incorporating ‘AI’.)

Leaving aside the “soft side” of AI, Shainline tackles the problem of scaling the necessary hardware infrastructure in terms of computation, networking, and memory. Leaning on brain-inspired spiking neural approaches and he gets into the weeds a bit.

Calling the effort “more akin to the construction of a fusion reactor or particle accelerator than a microchip,” Shainline wrote, “While there is much to be gained from artificial intelligence (AI) hardware at smaller scales, this article considers technological pathways to large cognitive systems, with tens to hundreds of billions of neurons, and communication infrastructure of commensurate complexity. Such technology will likely require many interconnected wafers, each packed densely with integrated circuits. We may refer to this field of research as neuromorphic supercomputing.”

Fascinating stuff.

Here’s his abstract and two figures from the paper:

“To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and electronics for computation are complementary and interdependent.

“Using light for communication enables high fan-out as well as low-latency signaling across large systems with no traffic-dependent bottlenecks. For computation, the inherent nonlinearities, high speed, and low power consumption of Josephson circuits are conducive to complex neural functions. Operation at 4 K enables the use of single-photon detectors and silicon light sources, two features that lead to efficiency and economical scalability.

“Here, I sketch a concept for optoelectronic hardware, beginning with synaptic circuits, continuing through wafer-scale integration, and extending to systems interconnected with fiber-optic tracts, potentially at the scale of the human brain and beyond.”

FIG. 2. Structure across scales. Biological systems have functional components from the nanometer scale (neurotransmitters, axonal pores) up to the full brain (0.3 m linear dimension for full human cerebral cortex). Speeds are limited by chemical diffusion and signal propagation along axons, which may ultimately limit the size of biological neural systems. The time constants associated with chemical diffusion and membrane charging/discharging span the range from 1 ms to 100 ms and dictate the speeds at which information processing occurs. The wall at the right indicates the communication-limited spatial-scaling barrier. Optoelectronic devices rarely have components with critical dimension smaller than 100 nm, and optoelectronic neurons are likely to be on the 100-lm scale, with dendritic arbor extending for millimeters and axonal arbor in some cases spanning the system. The time constants of these components can be engineered in hardware across a very broad range with high accuracy through circuit parameters, enabling rapid processing as well as long-term signal storage. Optical communication enables optoelectronic systems to extend far beyond the limits imposed by the slow conduction velocity of axons.
FIG. 5. Experimental progress toward superconducting optoelectronic networks. (a) Schematic of waveguide-integrated silicon LED. (b) Microscope image of a silicon LED waveguide-coupled to a superconducting-nanowire detector. (c) Experimental data showing that light is coupled through the waveguide, while crosstalk to an adjacent detector on the chip is suppressed by 40 dB. (a)–(c) Adapted from Buckley et al., Appl. Phys. Lett. 111, 141101 (2017). Copyright 2017 Author(s), licensed under a Creative Commons Attribution (CC BY) license. (d) Schematic of the superconducting thin-film amplifier. (e) and (f) The resistive switch driving the LED. (e) Square pulses are driven into the switch gate. (f) When the switch is driven, light is produced from the LED and detected by the SPD. (d)–(f) Adapted from McCaughan et al., Nat. Electron. 2, 451 (2019). Copyright 2019 Author(s), licensed under a Creative Commons Attribution (CC BY) license. (g) Schematic of multi-planar integrated waveguides for dense routing. Adapted from Chiles et al., APL Photonics 2, 116101 (2017). Copyright 2017 Author(s), licensed under a Creative Commons Attribution (CC BY) license. (h) Schematic of feed- forward network implemented with two planes of waveguides. (i) Data from an experimental demonstration of routing between nodes of a two-layer feed-forward network with all-to-all connectivity. (h) and (i) Adapted from Chiles et al., APL Photonics, 106101 (2018). Copyright 2018 Author(s), licensed under a Creative Commons Attribution (CC BY) license.

Pursuit of optoelectronics, using photonics for communications, is hardly new, even in mainstream computer research. Moving data has become a key bottleneck and many companies are seeking to unlock photonics’ potential. For example, see HPCwire coverage of Nvidia work on photonics, Crystal Ball Gazing at Nvidia: R&D Chief Bill Dally Talks Targets and Approach. An interesting side note is that Dally is rather dismissive of spiking neural network approaches for use in computing.

Shainline’s paper (Optoelectronic intelligence) is best read directly.

Link to paper: https://aip.scitation.org/doi/10.1063/5.0040567

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