Laurent Larger

Millimeter-sized whispering gallery mode resonators produced from bulk crystalline substrates show great potential as optoelectronics components but are still delicate to produce. We report here on an improved manufacturing technique based on femtosecond laser ablation, which allowed us to obtain state-of-the-art performance (quality factor around 10⁹) in calcium fluoride with only half the processing time required before. Our results are supported by optical profilometric and cavity ringdown measurements, and could be extended to a variety of other substrates.

Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experienced a revival. Here, we provide a general overview of progress over the past decade, and sketch a roadmap of important future developments. We focus on photonic implementations of the reservoir computing machine learning paradigm, which offers a conceptually simple approach that is amenable to hardware implementations. In particular, we provide an overview of photonic reservoir computing implemented via either spatio temporal or delay dynamical systems. Going beyond reservoir computing, we discuss recent advances and future challenges of photonic implementations of deep neural networks, of the quest for learning methods that are hardware-friendly as well as realizing autonomous photonic neural networks, i.e. with minimal digital electronic auxiliary hardware.

Nonlinear spatio-temporal systems are the basis for countless physical phenomena in such diverse fields as ecology, optics, electronics and neuroscience. The canonical approach to unify models originating from different fields is the normal form description, which determines the generic dynamical aspects and different bifurcation scenarios. Realizing different types of dynamical systems via one experimental platform that enables continuous transition between normal forms through tuning accessible system parameters is therefore highly relevant. Here, we show that a transmissive, optically-addressed spatial light modulator under coherent optical illumination and optical feedback coupling allows tuning between pitchfork, transcritical and saddle-node bifurcations of steady states. We demonstrate this by analytically deriving the system's normal form equations and confirm these results via extensive numerical simulations. Our model describes a nematic liquid crystal device using nano-dimensional dichalcogenide (a-As$_2$S$_3$) glassy thin-films as photo sensors and alignment layers, and we use device parameters obtained from experimental characterization. Optical coupling, for example using diffraction, holography or integrated unitary maps allow implementing a variety of system topologies of technological relevance for neural networks and potentially XY-Hamiltonian models with ultra low energy consumption.

Analog neural networks are promising candidates for overcoming the sever energy challenges of digital Neural Network processors. However, noise is an inherent part of analogue circuitry independent if electronic, optical or electro-optical integration is the target. I will discuss fundamental aspects of noise in analogue circuits and will then introduce our analytical framwork describing noise propagation in fully trained deep neural networks comprising nonlinear neurons. Most importantly, we found that noise accumulation can be very efficiently supressed under realistic hardware conditions. As such, neural networks implemented in analog hardware should be very robust to internal noise, which is of fundamental importance for future hardware realizations.

We study and analyze the fundamental aspects of noise propagation in recurrent as well as deep, multilayer networks. The motivation of our study is neural networks in analog hardware; yet, the methodology provides insight into networks in general. Considering noisy linear nodes, we investigate the signal-to-noise ratio at the network’s outputs, which determines the upper limit of computational precision. We consider additive and multiplicative noise, which can be purely local as well as correlated across populations of neurons. This covers the chief internal-perturbations of hardware networks, and noise amplitudes were obtained from a physically implemented neural network. Analytically derived descriptions agree exceptionally well with numerical data, enabling clear identification of the components critical for management and mitigation of noise. We find that analog neural networks are surprisingly robust, in particular, against noisy neurons. Their uncorrelated perturbations are almost fully suppressed, while correlated noise can accumulate. Our work identifies notoriously sensitive points while highlighting a surprising robustness of such computational systems.

Neural networks are transforming the field of computer algorithms, yet their emulation on current computing substrates is highly inefficient. Reservoir computing was successfully implemented on a large variety of substrates and gave new insight in overcoming this implementation bottleneck. Despite its success, the approach lags behind the state of the art in deep learning. We therefore extend time-delay reservoirs to deep networks and demonstrate that these conceptually correspond to deep convolutional neural networks. Convolution is intrinsically realized on a substrate level by generic drive-response properties of dynamical systems. The resulting novelty is avoiding vector matrix products between layers, which cause low efficiency in today’s substrates. Compared to singleton time-delay reservoirs, our deep network achieves accuracy improvements by at least an order of magnitude in Mackey-Glass and Lorenz time series prediction.