Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing

Tait, Alexander N.; Nahmias, Mitchell A.; Shastri, Bhavin J.; Prucnal, Paul R.

doi:10.1109/jlt.2014.2345652

Cited by 376 publications

(248 citation statements)

References 72 publications

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“…As saturable absorber they implemented a single layer of graphene. In 2014, the same group evaluated larger networks based on electro-optically excited semiconductor lasers [45]. Wavelength division multiplexing technology was used for addressing single lasers, which results in a limitation of ~60 nodes per chip.…”

Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

Advances in photonic reservoir computing

2017

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Abstract:We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir's complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

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Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

Advances in photonic reservoir computing

2017

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“…Other approaches that leverage phenomena unique to optics for neuromorphic computing [107][108][109][110][111][112] have employed optical devices such as lasers and integrated microresonators. Laser cavities with strong light-matter interaction can be leveraged to realize complex nonlinear dynamics which can emulate the behavior of neurons [107,109,112].…”

Section: A Advantages Of Optoelectronic Neural Networkmentioning

confidence: 99%

“…Laser cavities with strong light-matter interaction can be leveraged to realize complex nonlinear dynamics which can emulate the behavior of neurons [107,109,112]. The frequency selectivity of integrated ring resonators can be used to achieve synaptic weights [110]. Optical neural networks [111,113] and spiking neurons [109,[114][115][116][117] based on these effects have been proposed and demonstrated.…”

Section: A Advantages Of Optoelectronic Neural Networkmentioning

confidence: 99%

Superconducting Optoelectronic Circuits for Neuromorphic Computing

et al. 2017

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Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density necessary to scale to systems with enormous entropy. The proposed processing units can operate at speeds of at least 20 MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm 2 for neurons with 700 connections operating at full speed at 2 K. The processing units achieve an energy efficiency of ≈ 20 aJ per synapse event. By leveraging multilayer photonics with deposited waveguides and superconductors with feature sizes > 100 nm, this approach could scale to systems with massive interconnectivity and complexity for advanced computing as well as explorations of information processing capacity in systems with an enormous number of information-bearing microstates.

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“…A networkable photonic device with optical I/O, provided that it is capable of emulating an artificial neuron, is named a PNN [31]. Formulations of a photonic PNN can be divided into two main categories: all-optical and opticalelectrical-optical (O/E/O), respectively classified according to whether the information is always embedded in the optical domain or switches from optical to electrical and back.…”

Section: Photonic Neuron Module: Pnnmentioning

confidence: 99%

“…In this section, we discuss the challenges involving the creation of a network of neurons using photonic hardware, in particular, the creation of a weighted addition scheme for every PNN. Tait et al [31] proposed an integrated photonic neural networking scheme called "broadcast-and-weight" that uses WDM to support a large number of reconfigurable analog connections using silicon photonic device technology.…”

Section: Isomorphism To Biological Spiking Neuronmentioning

confidence: 99%

Progress in neuromorphic photonics

et al. 2017

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As society's appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.

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Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing

Cited by 376 publications

References 72 publications

Advances in photonic reservoir computing

Advances in photonic reservoir computing

Superconducting Optoelectronic Circuits for Neuromorphic Computing

Progress in neuromorphic photonics

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