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

Updates
0 new
2
Recommendations
0 new
0
Followers
0 new
16
Reads
0 new
161

Project log

Angelina Totović
added a research item
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.
Angelina Totović
added an update
PlasmoniAC Newsletter issue 1, November 2020 is online. You can read about the latest info and results of the project here:
 
Angelina Totović
added a research item
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.
Angelina Totović
added an update
Have a look at our official project video to learn more about PlasmoniAC's vision!
Prefer to read instead of watching? Check out our brochure in the attachment!
 
Angelina Totović
added a research item
Neuromorphic photonics came to the fore promising neural networks (NNs) with orders of magnitude higher computational speeds compared to electronic counterparts. In this direction, research efforts have been mainly concentrated on the development of spiking, convolutional and Feed-Forward (FF)-NN architectures, aiming to solve complex cognitive problems. However, in order to solve time-series classification and prediction complex tasks, state-of-the-art deep-learning models require in most cases the employment of Recurrent-NNs (RNNs) along with their gated variants, such as Long-Short-Term-Memories (LSTMs) and Gated-Recurrent-Units (GRUs). Herein, we experimentally demonstrate the first, to the best of our knowledge, all-optical RNN with a gating mechanism, laying the foundations for all-optical LSTMs and GRUs. The proposed layouts exploit a Semiconductor-Optical-Amplifier (SOA)-based sigmoid activation within a fiber loop and were validated using asynchronous Wavelength-Division-Multiplexed (WDM) signals with 100psec optical pulses. A SOA-Mach-Zehnder-Interferometer (SOA-MZI) gate was employed in the Gated-RNN version, with the RNN output defining the input signal fraction that is desired to enter the RNN. Finally, a complex NN architecture was trained using the FI-2010 financial dataset exploiting the proposed non-gated and gated-RNNs, showcasing in an outstanding F1 score of 41.68% and 41.85%, respectively, outperforming the Multi-Layer Perceptron (MLP) based models by 6.49% in average.
Angelina Totović
added a research item
We demonstrate experimentally, the first all-optical recurrent-neuron with a sigmoid activation function and four WDM-inputs with 100psec pulses. The proposed neuron geared up a neural-network for financial prediction-tasks exhibiting an accuracy of 42.57% on FI-2010.
Angelina Totović
added a research item
Neuromorphic photonics aims to transfer the high-bandwidth and low-energy credentials of optics into neuromorphic computing architectures, intending to mimic the architectural principles of brain-inspired computational machines via light-enabled artificial neurons. In this effort, photonic neurons are trying to combine the optical interconnect segments with functional optics that can realize all critical constituent neuromorphic functions, including the linear neuron stage and the activation function. However, complying with the typical requirements of well-established neural network training models for smoothly synergizing the photonic hardware with the best-in-class training algorithms, a linear photonic neuron has to be able to handle both positive and negative weight values, while the activation response has to closely follow widely used mathematical activation functions. Herein, we demonstrate a coherent linear neuron architecture that relies on a dual-IQ modulation cell as its basic neuron element, introducing distinct optical elements for weight amplitude and weight sign representation and exploiting binary optical carrier phase-encoding for positive/negative number representation. We present experimental results of a typical IQ modulator performing as an elementary two-input linear neuron cell and successfully implementing all-optical linear algebraic operations with 100-ps long optical pulses. We also provide the theoretical proof and formulation of how to extend a dual-IQ modulation cell into a complete N-input coherent linear neuron stage that requires only a single-wavelength optical input and avoids the resource-consuming Wavelength Division Multiplexing (WDM) weighting schemes. An 8-input coherent linear neuron is then combined with an experimentally validated optical sigmoid activation function into a physical layer simulation environment, with respective training and physical layer simulation results for the MNIST dataset revealing an average accuracy of 97.24% and 94.37%, respectively.
Angelina Totović
added a research item
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.
Angelina Totović
added a research item
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 ${\rm{TMAC/s/mm^{2}}}$ .
Angelina Totović
added a project 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.