Energy- and size-efficient ultra-fast plasmonic circuits for neuromorphic computing architectures
Energy- and size-efficient ultra-fast plasmonic circuits for neuromorphic computing architectures
Energy- and size-efficient ultra-fast plasmonic circuits for neuromorphic computing architectures

Concept


PlasmoniAC invests in neuromorphic computing towards sustaining processing power and energy efficiency scaling, adopting the best-in-class material and technology platforms for optimizing computational power, size and energy at every of its constituent functions. It employs the proven high-bandwidth and low-loss credentials of photonic interconnects together with the nm-size memory function of memristor nanoelectronics, bridging them by introducing plasmonics as the ideal technology for offering photonic-level bandwidths and electronic-level footprint computations within ultra-low energy consumption envelopes.

Following a holistic hardware/software co-design approach, PlasmoniAC targets the following objectives:

  1. to elevate plasmonics into a computationally-credible platform with Nx100 Gb/s bandwidth, μm2-scale size and >100 TMAC/s/W computational energy efficiency, using CMOS compatible materials for electro- and thermo-optic computational functions
  2. to blend them via a powerful 3D co-integration platform with SiN-based photonic interconnects and with non-volatile memristor-based weight control
  3. to fabricate two different sets of 100 Gb/s 16- and 8-fan-in linear plasmonic neurons
  4. to deploy a whole new class of plasmo-electronic and nanophotonic activation modules
  5. to demonstrate a full-set of sin2(x), ReLU, sigmoid and tanh plasmonic neurons for feed-forward and recurrent neurons
  6. to embrace them into a properly adapted Deep Learning training model suite, ultimately delivering a neuromorphic plasmonic software design library
  7. to apply them on IT security-oriented applications for threat and malware detection

Succeeding in its targets will release a powerful artificial plasmonic neuron suite with up to 3 orders of magnitude higher computational efficiencies per neuron and 1 and 6 orders of magnitude higher energy and footprint efficiencies, respectively, compared to the top state-of-the-art neuromorphic machines.

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