Project

# H2020 PlasmoniAC

Goal: PlasmoniAC – “Energy- and Size-Efficient Ultra-Fast Plasmonic Circuits for Neuromorphic Computing Architectures” – is a new 3-year long EU-funded project, under the H2020-ICT-06-2019: Unconventional Nanoelectronics Call, launched on January 1st, 2020, aiming to release a whole new class of energy- and size-efficient feed-forward and recurrent artificial plasmonic neurons with up to 100 GHz clock frequencies and 1 and 6 orders of magnitude better energy- and footprint-efficiencies, comparing to the current state-of-the art.

Date: 1 January 2020 - 31 December 2022

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## Project log

Neuromorphic photonics has turned into a key research area for enabling neuromorphic computing at much higher data-rates compared to their electronic counterparts, improving significantly the (Multiply-and-Accumulate) MAC/sec. At the same time, time-series classification problems comprise a large class of Artificial Intelligence (AI) applications where speed and latency can have a decisive role in their hardware deployment roadmap, highlighting the need for ultra-fast hardware implementations of simplified Recurrent Neural Networks (RNN) that can be extended in more advanced Long-Short-Term-Memory (LSTM) and Gated Recurrent Unit (GRU) machines. Herein, we experimentally demonstrate a novel Photonic Recurrent Neuron (PRN) for classifying successfully a time-series vector with 100-psec optical pulses and up to 10Gb/s data speeds, reporting on the fastest all-optical real-time classifier. Experimental classification of 3-bit optical binary data streams is presented, revealing an average accuracy of >91% and confirming the potential of PRNs to boost speed and latency performance in time-series AI applications.
PlasmoniAC Newsletter issue 1, November 2020 is online. You can read about the latest info and results of the project here:

The identification of neuromorphic computing as a highly promising alternative computing system has been emerged from its potential to increase rapidly the computational efficiency that is currently restricted by Moore’s law end. First electronic neuromorphic chips like IBM’s TrueNorth and Intel’s Loihi revealed a tremendous performance improvement in terms of computational speed and density; however, they are still operating in MHz rates. To this end, neuromorphic photonic integrated circuits can further increase the computational speed and density, having a large portfolio of components with GHz-bandwidth and low-energy. Herein, we present an all-optical sigmoid activation function as well as a single-λ linear neuron. The all-optical sigmoid activation function comprises a Semiconductor Optical Amplifier-Mach-Zehnder Interferometer (SOA-MZI) configured in differentially-biased scheme followed by an SOA. Its thresholding capabilities have been experimentally demonstrated with 100psec optical pulses. Then, we introduce an all-optical phase-encoded weighting scheme and we experimentally demonstrate its linear algebra operational credentials by the means of a typical IQ modulator operated at 10Gbaud/s.
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Photonic artificial neural networks have garnered enormous attention due to their potential to perform multiply-accumulate (MAC) operations at much higher clock rates and consuming significantly lower power and chip real-estate compared to digital electronic alternatives. Herein, we present a comprehensive power consumption analysis of photonic neurons, taking into account global design parameters and concluding to analytical expressions for the neuron's energy- and footprint efficiencies. We identify the optimal design-space and analyze the performance plateaus and their dependence on a range of physical parameters, highlighting the existence of an optimal data-rate for maximizing the energy efficiency. Following a survey of the best-in-class integrated photonic devices, including on-chip lasers, photodetectors, modulators and weighting elements, the mathematically calculated energy and footprint efficiencies are mapped into real photonic neuron deployment scenarios. We reveal that silicon photonics can compete with the best-performing currently available digital electronic neural network engines, reaching ${\rm{TMAC/s/mm^{2}}}$ footprint- and sub-pJ/MAC energy efficiencies. Simultaneously, neuromorphic plasmonics, plasmo-photonics and sub-wavelength photonics hold the credentials for 1 to 3 orders of magnitude improvements even when the laser requirements and a reasonable waveguide pitch are accounted for, promising performance at a few fJ/MAC and up to a few ${\rm{TMAC/s/mm^{2}}}$ .