Abstract
In recent years, the explosive development of artificial intelligence implementing by artificial neural networks (ANNs) creates inconceivable demands for computing hardware. However, conventional computing hardware based on electronic transistor and von Neumann architecture cannot satisfy such an inconceivable demand due to the unsustainability of Moore’s Law and the failure of Dennard’s scaling rules. Fortunately, analog optical computing offers an alternative way to release unprecedented computational capability to accelerate varies computing drained tasks. In this article, the challenges of the modern computing technologies and potential solutions are briefly explained in Chapter 1. In Chapter 2, the latest research progresses of analog optical computing are separated into three directions: vector/matrix manipulation, reservoir computing and photonic Ising machine. Each direction has been explicitly summarized and discussed. The last chapter explains the prospects and the new challenges of analog optical computing.
Highlights
The extraordinary development of complementary-metal-oxide-semiconductor (CMOS) technology facilitates an unprecedented success of integrated circuits
Moore’s law has made central processor units (CPUs) 300 times faster than that in 1990. Such an incredible development is unsustainable as predicted by the International Technology Roadmap of Semiconductors (ITRS) in 2016
The proliferation of artificial intelligence (AI) applications create exponentially increasing amounts of data that can hardly processed by conventional computing systems and architectures
Summary
The extraordinary development of complementary-metal-oxide-semiconductor (CMOS) technology facilitates an unprecedented success of integrated circuits. Both schemes are severely limited by the scale of the interaction network since practical applications requires large amount of spin node. To the best of our knowledge, the largest MZI matrix (64*64) developed by Lightmatter is still smaller than the dimension of practical models [42] Nonlinear effect, such as frequency conversion via χ(2) / χ(3) medium [154, 158, 159], could be a viable approach to build interaction network on a large scale.
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