In plain words: A proposed neuron design sends signals as single particles of light, caught by super-cold detectors, so one neuron can reach many others quickly using little energy. Unlike ordinary electrical wiring, it combines signals cheaply, but light's speed limits how large the network can grow.
Abstract
The design of neural hardware is informed by the prominence of differentiated processing and information integration in cognitive systems. The central role of communication leads to the principal assumption of the hardware platform: signals between neurons should be optical to enable fanout and communication with minimal delay. The requirement of energy efficiency leads to the utilization of superconducting detectors to receive single-photon signals. We discuss the potential of superconducting optoelectronic hardware to achieve the spatial and temporal information integration advantageous for cognitive processing, and we consider physical scaling limits based on light-speed communication. We introduce the superconducting optoelectronic neurons and networks that are the subject of the subsequent papers in this series.
Jeffrey M. Shainline, Sonia M. Buckley, Adam N. McCaughan, Jeff Chiles, Richard P. Mirin, Sae Woo Nam
arXiv:1805.01929 · cs.NE, cs.ET · submitted May 4, 2018 · updated May 24, 2018
abstract · pdf · html · 10 pages, 1 figure
Superconducting Optoelectronic Neurons II: Receiver Circuits: https://arxiv.org/abs/1805.02599
Superconducting Optoelectronic Neurons III: Synaptic Plasticity: https://arxiv.org/abs/1805.01937
Superconducting Optoelectronic Neurons IV: Transmitter Circuits: https://arxiv.org/abs/1805.01941
Superconducting Optoelectronic Neurons V: Networks and Scaling: https://arxiv.org/abs/1805.01942
It's so ambitious and so questionable at the same time. I'd classify it as hard science fiction, like a really good Orion's Arm entry, if the authors weren't from NIST.
Final two discussion paragraphs from paper V:
While it may be difficult to build systems larger than 10 billion neurons in the near term, such a system is not physically limited. Like the brain, such limits will be incurred due to the velocity of signal propagation. From Fig. 6(c) we know that networks as large as data centers can sustain coherent oscillations at 1 MHz. Such a facility would house 10^8 300 mm wafers if they were stacked 100 deep. This would result in 100 trillion neurons per data center across modules interconnected with another power law distribution.
Networks need not oscillate at 1 MHz, and if they supported system-wide activity at 1 kHz—faster than any oscillation of the human brain—the neuronal pool could occupy a significant fraction of the earth’s surface and employ quintillions of neurons. We do not wish to cover earth in such devices, but asteroids provide ample, uncontroversial real estate. The materials for this hardware are abundant on M-type and S-type asteroids [76–80]. It appears possible for an asteroid belt to form the nodes and light to form the edges of a solar-system scale intelligent network. Asteroids can be separated by billions of meters, so light-speed communication delays may be several seconds or longer. For cognitive systems oscillating up to 20 MHz, such delays would cause individual modules to operate as separate cognitive systems, much like a society of humans.
Apart from the breathtaking scale of speculation -- which one could admittedly also find to good effect in older papers about e.g. nuclear power -- there is a more concrete question. What's the all-in energy cost-per-operation vs. conventional hardware, CMOS devices operating above room temperature? Operating at liquid helium temperatures dramatically shrinks the on-chip power demand, and then the cryocooler dramatically re-inflates it. Lab scale production of liquid helium takes ~570 watts of wall-plug power to produce 1 watt of cooling near 4.2 K [1]. At the wall plug, cryogenic cooling systems included, how does this design compare to existing hardware on power and speed for neural network training or inference? AFAICT, the authors do not attempt to estimate such a figure of merit.
[1] Basics of low-temperature refrigeration: https://cds.cern.ch/record/1974048/files/arXiv:1501.07392.pd...