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N. Pleros

Publications and source records attributed to N. Pleros.

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On-chip 1 TOPS Hyperdimensional Photonic Tensor Core using a WDM Silicon Photonic Coherent Crossbar

We demonstrate an on-chip 0.96 TOPS hyperdimensional photonic tensor core by utilizing a time-spacewavelength multiplexed silicon photonic Crossbar (Xbar). The novel architecture relies on serializing the large matrix-vector or tensor-vector products by unfolding multiply and accumulation operations over time domain, while simultaneously distributing the computational workload over different spatial and wavelength channels. We experimentally demonstrate the operation of a 4-channel 2-input TSWDM Xbar that incorporates 56 GHz electroabsorption modulators (EAMs) and 4-channel integrated multiplexing stages. Its successful operation as a 4x2x1 tensorvector multiplication unit demonstrated an average error of 3.9%. Its performance as a photonic AI accelerator was also evaluated in the classification task of the Iris dataset, presenting experimental accuracies of 93.3% at data rates between 4x10 and 4x30 GBd, reaching 83.3% when the data rate increases to 4x60 GBd. Finally, we discuss the TSWDM Xbar scalability potential, revealing that the inclusion of a WDM scheme in the SDM architecture reduces the operating laser power, feasibly boosting the potential of constructing photonic accelerators with computational throughput in the POPS regime.

physics.optics

Photonic Neural Networks and Optics-informed Deep Learning Fundamentals

The recent explosive compute growth, mainly fueled by the boost of AI and DNNs, is currently instigating the demand for a novel computing paradigm that can overcome the insurmountable barriers imposed by conventional electronic computing architectures. PNNs implemented on silicon integration platforms stand out as a promising candidate to endow NN hardware, offering the potential for energy efficient and ultra-fast computations through the utilization of the unique primitives of photonics i.e. energy efficiency, THz bandwidth and low-latency. Thus far, several demonstrations have revealed the huge potential of PNNs in performing both linear and non-linear NN operations at unparalleled speed and energy consumption metrics. Transforming this potential into a tangible reality for DL applications requires, however, a deep understanding of the basic PNN principles, requirements and challenges across all constituent architectural, technological and training aspects. In this tutorial, we, initially, review the principles of DNNs along with their fundamental building blocks, analyzing also the key mathematical operations needed for their computation in a photonic hardware. Then, we investigate, through an intuitive mathematical analysis, the interdependence of bit precision and energy efficiency in analog photonic circuitry, discussing the opportunities and challenges of PNNs. Followingly, a performance overview of PNN architectures, weight technologies and activation functions is presented, summarizing their impact in speed, scalability and power consumption. Finally, we provide an holistic overview of the optics-informed NN training framework that incorporates the physical properties of photonic building blocks into the training process in order to improve the NN classification accuracy and effectively elevate neuromorphic photonic hardware into high-performance DL computational settings.

physics.optics