Roadmap on emerging hardware and technology for machine learning.

Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.

Lateral bipolar junction transistor on a silicon photonics platform.

Integration of active electronics into photonic systems is necessary for large-scale photonic integration. While heterogeneous integration leverages high-performance electronics, a monolithic scheme can coexist by aiding the electronic processing, improving overall efficiency. We report a lateral bipolar junction transistor on a commercial silicon photonics foundry process. We achieved a DC current gain of 10 with a Darlington configuration, and using measured S-parameters for a single BJT, the available AC gain was at least 3dB for signal frequencies up to 1.1 GHz. Our single BJT demonstrated a transimpedance of 3.2mS/µm, which is about 70 times better than existing literature.

Photonic independent component analysis using an on-chip microring weight bank.

Independent component analysis (ICA) is a general-purpose technique for analyzing multi-dimensional data to reveal the underlying hidden factors that are maximally independent from each other. We report the first photonic ICA on mixtures of unknown signals by employing an on-chip microring (MRR) weight bank. The MRR weight bank performs so-called weighted addition (i.e., multiply-accumulate) operations on the received mixtures, and outputs a single reduced-dimensional representation of the signal of interest. We propose a novel ICA algorithm to recover independent components solely based on the statistical information of the weighted addition output, while remaining blind to not only the original sources but also the waveform information of the mixtures. We investigate both channel separability and near-far problems, and our two-channel photonic ICA experiment demonstrates our scheme holds comparable performance with the conventional software-based ICA method. Our numerical simulation validates the fidelity of the proposed approach, and studies noise effects to identify the operating regime of our method. The proposed technique could open new domains for future research in blind source separation, microwave photonics, and on-chip information processing.

Optimization of photoluminescence from W centers in a silicon-on-insulator.

W centers are trigonal defects generated by self-ion implantation in silicon that exhibit photoluminescence at 1.218 µm. We have shown previously that they can be used in waveguide-integrated all-silicon light-emitting diodes (LEDs). Here we optimize the implant energy, fluence and anneal conditions to maximize the photoluminescence intensity for W centers implanted in silicon-on-insulator, a substrate suitable for waveguide-integrated devices. After optimization, we observe near two orders of magnitude improvement in photoluminescence intensity relative to the conditions with the stopping range of the implanted ions at the center of the silicon device layer. The previously demonstrated waveguide-integrated LED used implant conditions with the stopping range at the center of this layer. We further show that such light sources can be manufactured at the 300-mm scale by demonstrating photoluminescence of similar intensity from 300 mm silicon-on-insulator wafers. The luminescence uniformity across the entire wafer is within the measurement error.

Blind source separation with integrated photonics and reduced dimensional statistics.

Microwave communications have witnessed an incipient proliferation of multi-antenna and opportunistic technologies in the wake of an ever-growing demand for spectrum resources, while facing increasingly difficult network management over widespread channel interference and heterogeneous wireless broadcasting. Radio frequency (RF) blind source separation (BSS) is a powerful technique for demixing mixtures of unknown signals with minimal assumptions, but relies on frequency dependent RF electronics and prior knowledge of the target frequency band. We propose photonic BSS with unparalleled frequency agility supported by the tremendous bandwidths of photonic channels and devices. Specifically, our approach adopts an RF photonic front-end to process RF signals at various frequency bands within the same array of integrated microring resonators, and implements a novel two-step photonic BSS pipeline to reconstruct source identities from the reduced dimensional statistics of front-end output. We verify the feasibility and robustness of our approach by performing the first proof-of-concept photonic BSS experiments on mixed-over-the-air RF signals across multiple frequency bands. The proposed technique lays the groundwork for further research in interference cancellation, radio communications, and photonic information processing.

Photonic principal component analysis using an on-chip microring weight bank.

Photonic principal component analysis (PCA) enables high-performance dimensionality reduction in wideband analog systems. In this paper, we report a photonic PCA approach using an on-chip microring (MRR) weight bank to perform weighted addition operations on correlated wavelength-division multiplexed (WDM) inputs. We are able to configure the MRR weight bank with record-high accuracy and precision, and generate multi-channel correlated input signals in a controllable manner. We also consider the realistic scenario in which the PCA procedure remains blind to the waveforms of both the input signals and weighted addition output, and propose a novel PCA algorithm that is able to extract principal components (PCs) solely based on the statistical information of the weighted addition output. Our experimental demonstration of two-channel photonic PCA produces PCs holding consistently high correspondence to those computed by a conventional software-based PCA method. Our numerical simulation further validates that our scheme can be generalized to high-dimensional (up to but not limited to eight-channel) PCA with good convergence. The proposed technique could bring new solutions to problems in microwave communications, ultrafast control, and on-chip information processing.

Neuromorphic photonics with electro-absorption modulators.

Photonic neural networks benefit from both the high-channel capacity and the wave nature of light acting as an effective weighting mechanism through linear optics. Incorporating a nonlinear activation function by using active integrated photonic components allows neural networks with multiple layers to be built monolithically, eliminating the need for energy and latency costs due to external conversion. Interferometer-based modulators, while popular in communications, have been shown to require more area than absorption-based modulators, resulting in a reduced neural network density. Here, we develop a model for absorption modulators in an electro-optic fully connected neural network, including noise, and compare the network's performance with the activation functions produced intrinsically by five types of absorption modulators. Our results show the quantum well absorption modulator-based electro-optic neuron has the best performance allowing for 96% prediction accuracy with 1.7×10-12 J/MAC excluding laser power when performing MNIST classification in a 2 hidden layer feed-forward photonic neural network.

Feedback control for microring weight banks.

Microring weight banks present novel opportunities for reconfigurable, high-performance analog signal processing in photonics. Controlling microring filter response is a challenge due to fabrication variations and thermal sensitivity. Prior work showed continuous weight control of multiple wavelength-division multiplexed signals in a bank of microrings based on calibration and feedforward control. Other prior work has shown resonance locking based on feedback control by monitoring photoabsorption-induced changes in resistance across in-ring photoconductive heaters. In this work, we demonstrate continuous, multi-channel control of a microring weight bank with an effective 5.1 bits of accuracy on 2Gbps signals. Unlike resonance locking, the approach relies on an estimate of filter transmission versus photo-induced resistance changes. We introduce an estimate still capable of providing 4.2 bits of accuracy without any direct transmission measurements. Furthermore, we present a detailed characterization of this response for different values of carrier wavelength offset and power. Feedback weight control renders tractable the weight control problem in reconfigurable analog photonic networks.

Two-pole microring weight banks.

Weighted addition is an elemental multi-input to single-output operation that can be implemented with high-performance photonic devices. Microring (MRR) weight banks bring programmable weighted addition to silicon photonics. Prior work showed that their channel limits are affected by coherent inter-channel effects that occur uniquely in weight banks. We fabricate two-pole designs that exploit this inter-channel interference in a way that is robust to dynamic tuning and fabrication variation. Scaling analysis predicts a channel count improvement of 3.4-fold, which is substantially greater than predicted by incoherent analysis used in conventional MRR devices. Advances in weight bank design expand the potential of reconfigurable analog photonic networks and multivariate microwave photonics.

Simultaneous excitatory and inhibitory dynamics in an excitable laser.

Neocortical systems encode information in electrochemical spike timings, not just mean firing rates. Learning and memory in networks of spiking neurons is achieved by the precise timing of action potentials that induces synaptic strengthening (with excitation) or weakening (with inhibition). Inhibition should be incorporated into brain-inspired spike processing in the optical domain to enhance its information-processing capability. We demonstrate the simultaneous excitatory and inhibitory dynamics in an excitable (i.e., a pulsed) laser neuron, both numerically and experimentally. We investigate the bias strength effect, inhibitory strength effect, and excitatory and inhibitory input timing effect, based on the simulation platform of an integrated graphene excitable laser. We further corroborate these analyses with proof-of-principle experiments utilizing a fiber-based graphene excitable laser, where we introduce inhibition by directly modulating the gain of the laser. This technology may potentially open novel spike-processing functionality for future neuromorphic photonic systems.

Multi-channel control for microring weight banks.

We demonstrate 4-channel, 2GHz weighted addition in a silicon microring filter bank. Accurate analog weight control becomes more difficult with increasing number of channels, N, as feedback approaches become impractical and brute force feedforward approaches take O(2N) calibration measurements in the presence of inter-channel dependence. We introduce model-based calibration techniques for thermal cross-talk and cross-gain saturation, which result in a scalable O(N) calibration routine and 3.8 bit feedforward weight accuracy on every channel. Practical calibration routines are indispensible for controlling large-scale microring systems. The effect of thermal model complexity on accuracy is discussed. Weighted addition based on silicon microrings can apply the strengths of photonic manufacturing, wideband information processing, and multiwavelength networks towards new paradigms of ultrafast analog distributed processing.

Demonstration of WDM weighted addition for principal component analysis.

We consider an optical technique for performing tunable weighted addition using wavelength-division multiplexed (WDM) inputs, the enabling function of a recently proposed photonic spike processing architecture [J. Lightwave Technol., 32 (2014)]. WDM weighted addition provides important advantages to performance, integrability, and networking capability that were not possible in any past approaches to optical neurocomputing. In this letter, we report a WDM weighted addition prototype used to find the first principal component of a 1Gbps, 8-channel signal. Wideband, multivariate techniques have immediate relevance to modern radio systems, and photonic spike processing networks enabled by WDM could open new domains of information processing that bring unprecedented bandwidth and intelligence to problems in radio communications, ultrafast control, and scientific computing.

Excitable laser processing network node in hybrid silicon: analysis and simulation.

The combination of ultrafast laser dynamics and dense on-chip multiwavelength networking could potentially address new domains of real-time signal processing that require both speed and complexity. We present a physically realistic optoelectronic simulation model of a circuit for dynamical laser neural networks and verify its behavior. We describe the physics, dynamics, and parasitics of one network node, which includes a bank of filters, a photodetector, and excitable laser. This unconventional circuit exhibits both cascadability and fan-in, critical properties for the large-scale networking of information processors based on laser excitability. In addition, it can be instantiated on a photonic integrated circuit platform and requires no off-chip optical I/O. Our proposed processing system could find use in emerging applications, including cognitive radio and low-latency control.

SIMPEL: circuit model for photonic spike processing laser neurons.

We propose an equivalent circuit model for photonic spike processing laser neurons with an embedded saturable absorber­a simulation model for photonic excitable lasers (SIMPEL). We show that by mapping the laser neuron rate equations into a circuit model, SPICE analysis can be used as an efficient and accurate engine for numerical calculations, capable of generalization to a variety of different types of laser neurons with saturable absorber found in literature. The development of this model parallels the Hodgkin-Huxley model of neuron biophysics, a circuit framework which brought efficiency, modularity, and generalizability to the study of neural dynamics. We employ the model to study various signal-processing effects such as excitability with excitatory and inhibitory pulses, binary all-or-nothing response, and bistable dynamics.

WDM optical steganography based on amplified spontaneous emission noise.

We propose and experimentally demonstrate a wavelength-division multiplexed (WDM) optical stealth transmission system carried by amplified spontaneous emission (ASE) noise. The stealth signal is hidden in both time and frequency domains by using ASE noise as the signal carrier. Each WDM channel uses part of the ASE spectrum, which provides more flexibility to apply stealth transmission in a public network and adds another layer of security to the stealth channel. Multi-channel transmission also increases the overall channel capacity, which is the major limitation of the single stealth channel transmission based on ASE noise. The relations between spectral bandwidth and coherence length of ASE carrier have been theoretically analyzed and experimentally investigated.

Photonic online learning: a perspective.

Emerging neuromorphic hardware promises to solve certain problems faster and with higher energy efficiency than traditional computing by using physical processes that take place at the device level as the computational primitives in neural networks. While initial results in photonic neuromorphic hardware are very promising, such hardware requires programming or "training" that is often power-hungry and time-consuming. In this article, we examine the online learning paradigm, where the machinery for training is built deeply into the hardware itself. We argue that some form of online learning will be necessary if photonic neuromorphic hardware is to achieve its true potential.

Neuromorphic photonic networks using silicon photonic weight banks.

Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using "neural compiler" to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.

Spike processing with a graphene excitable laser.

Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved "spiking" of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation--fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms.