Bayesian Photonic Accelerators for Energy Efficient and Noise Robust Neural Processing

Artificial neural networks are efficient computing platforms inspired by the brain. Such platforms can tackle a vast area of real-life tasks ranging from image processing to language translation. Silicon photonic integrated chips (PICs), by employing coherent interactions in Mach-Zehnder interferometers, are promising accelerators offering record low power consumption and ultra-fast matrix multiplication. Such photonic accelerators, however, suffer from phase uncertainty due to fabrication errors and crosstalk effects that inhibit the development of high-density implementations. In this work, we present a Bayesian learning framework for such photonic accelerators. In addition to the conventional log-likelihood optimization path, two novel training schemes are derived, namely a regularized version and a fully Bayesian learning scheme. They are applied on a photonic neural network with 512 phase shifters targeting the MNIST dataset. The new schemes, when combined with a pre-characterization stage that provides the passive offsets, are able to dramatically decrease the operational power of the PIC beyond 70%, with just a slight loss in classification accuracy. The full Bayesian scheme, apart from this energy reduction, returns information with respect to the sensitivity of the phase shifters. This information is used to de-activate 31% of the phase actuators and, thus, significantly simplify the driving system.</p

Bayesian Training in Photonic Neural Meshes

Neural networks based on reconfigurable photonic integrated chips (RPICs) can offer zero-latency processing, marginal power consumption and operational flexibility. On the other hand, they are subject to, performance affecting, operational/fabrication deviations in their building blocks. Here, we present a Bayesian learning framework that when combined with device characterization, can dramatically decrease power consumption beyond 74% and significantly simplify the driving circuitry.</p

Time-Multiplexed Spiking Convolutional Neural Network Based on VCSELs for Unsupervised Image Classification

In this work, we present numerical results concerning a multilayer “deep” photonic spiking convolutional neural network, arranged so as to tackle a 2D image classification task. The spiking neurons used are typical two-section quantum-well vertical-cavity surface-emitting lasers that exhibit isomorphic behavior to biological neurons, such as integrate-and-fire excitability and timing encoding. The isomorphism of the proposed scheme to biological networks is extended by replicating the retina ganglion cell for contrast detection in the photonic domain and by utilizing unsupervised spike dependent plasticity as the main training technique. Finally, in this work we also investigate the possibility of exploiting the fast carrier dynamics of lasers so as to time-multiplex spatial information and reduce the number of physical neurons used in the convolutional layers by orders of magnitude. This last feature unlocks new possibilities, where neuron count and processing speed can be interchanged so as to meet the constraints of different applications

High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks

AbstractNeuromorphic computing using photonic hardware is a promising route towards ultrafast processing while maintaining low power consumption. Here we present and numerically evaluate a hardware concept for realizing photonic recurrent neural networks and reservoir computing architectures. Our method, called Recurrent Optical Spectrum Slicing Neural Networks (ROSS-NNs), uses simple optical filters placed in a loop, where each filter processes a specific spectral slice of the incoming optical signal. The synaptic weights in our scheme are equivalent to the filters’ central frequencies and bandwidths. Numerical application to high baud rate optical signal equalization (&gt;100 Gbaud) reveals that ROSS-NN extends optical signal transmission reach to &gt; 60 km, more than four times that of two state-of-the-art digital equalizers. Furthermore, ROSS-NN relaxes complexity, requiring less than 100 multiplications/bit in the digital domain, offering tenfold reduction in power consumption with respect to these digital counterparts. ROSS-NNs hold promise for efficient photonic hardware accelerators tailored for processing high-bandwidth (&gt;100 GHz) optical signals in optical communication and high-speed imaging applications.</jats:p

Unconventional Integrated Photonic Accelerators for High-Throughput Convolutional Neural Networks

We provide an overview of the rapidly evolving landscape of integrated photonic neuromorphic architectures, specifically targeting the implementation of convolutional neural networks. The exploding research momentum stems from the well-known advantages of photonic circuits compared to digital electronics, and at the same time, it is driven by the massive need for cognitive image/video processing. In this context, we provide a detailed literature review on photonic cores operating as convolutional neural networks, covering either the functionality of a conventional neural network or its spiking counterpart. Moreover, we propose 2 alternative photonic approaches that refrain from simply transferring neural network concepts directly into the optical domain; instead, they focus on fusing photonic, digital electronic, and event-based bioinspired processing to optimally exploit the virtues of each scheme. These approaches can offer beyond state-of-the-art performance while relying on realistic, scalable technology. The first approach is based on a photonic integrated platform and a bioinspired spectrum-slicing technique. The photonic chip allows feature extraction through optical filtering with low power consumption and an equivalent computational efficiency of 72 femtojoules per multiply-and-accumulate operation for 5-bit precision. When combined with typical digital neural networks, an almost 5-fold reduction in the number of parameters was achieved with a minor loss of accuracy compared to established convolutional neural networks. The second approach follows a bioisomorphic route in which miniaturized spiking laser neurons and unsupervised bioinspired training are unified in a deep architecture, revealing a noise-resilient and power-efficient proposition