Novel frontier of photonics for data processing—Photonic accelerator

Abstract

In the emerging Internet of things cyber-physical system-embedded society, big data analytics needs huge computing capability with better energy efficiency. Coming to the end of Moore’s law of the electronic integrated circuit and facing the throughput limitation in parallel processing governed by Amdahl’s law, there is a strong motivation behind exploring a novel frontier of data processing in post-Moore era. Optical fiber transmissions have been making a remarkable advance over the last three decades. A record aggregated transmission capacity of the wavelength division multiplexing system per a single-mode fiber has reached 115 Tbit/s over 240 km. It is time to turn our attention to data processing by photons from the data transport by photons. A photonic accelerator (PAXEL) is a special class of processor placed at the front end of a digital computer, which is optimized to perform a specific function but does so faster with less power consumption than an electronic general-purpose processor. It can process images or time-serial data either in an analog or digital fashion on a real-time basis. Having had maturing manufacturing technology of optoelectronic devices and a diverse array of computing architectures at hand, prototyping PAXEL becomes feasible by leveraging on, e.g., cutting-edge miniature and power-efficient nanostructured silicon photonic devices. In this article, first the bottleneck and the paradigm shift of digital computing are reviewed. Next, we review an array of PAXEL architectures and applications, including artificial neural networks, reservoir computing, pass-gate logic, decision making, and compressed sensing. We assess the potential advantages and challenges for each of these PAXEL approaches to highlight the scope for future work toward practical implementation.

Integrated photonic metasystem for image classifications at telecommunication wavelength

“…The cascaded layers of phase-only transmissive arrays are trained by deep diffractive neuron networks [8]. Compared to the waveguide based integrated photonic processors [31][32][33], the metasystem architecture offers three orders of magnitude higher throughput of vector-bymatrix multiplication (Supplementary Note S1) [34]. The metamaterial manifested weight element density, combined with diffraction strengthen inter-layer connectivity, enables the passive system to accomplish machine learning tasks of spatial pattern classification (Fig.…”

Reservoir Computing Based on Two Parallel Reservoirs Under Identical Electrical Message Injection

“…Different from traditional recurrent neural network algorithm, RC's connection weights of the input layer and the reservoir are fixed during training procedure, and only the readout weights need to be trained through a straightforward linear regression method. Since the training procedure only involves the readout layer without changing the reservoir connection, RC possesses such unique virtues as fast training process and easy hardware implementation [4]. Traditionally, RC is implemented based on a spatially distributed network [5]- [7], where the reservoir is constituted by numerous interconnected nonlinear nodes and the reservoir states (the states of nonlinear nodes) are obtained in parallel.…”

“…In recent years, though relevant researches on SL-based time-delay RC have made significant progresses [19]- [31], the data processing rate still needs to be further improved to meet the requirement of fast information processing [4]. In a SL-based time-delay RC system, an original data is firstly sampled and held for a period T. Next, the resulted signal is multiplied by a temporal mask to increase the dimensionality of the state space.…”

Entangled and correlated photon mixed strategy for social decision making

“…Recently, photonic approaches to decision-making problems have been intensively studied using single photons 12 , 13 , chaotic lasers 14 , 15 , and entangled photons 16 . Related topics are discussed in recent review articles 1 , 17 , 18 .…”