SC17 Denver, CO

P50: Energy-Efficient and Scalable Bio-Inspired Nanophotonic Computing

Authors: Mohammadamin Nazirzadeh (University of California, Davis), Pouya Fotouhi (University of California, Davis), Mohammadsadegh Shamsabardeh (University of California, Davis), Roberto Proietti (University of California, Davis), S. J. Ben Yoo (University of California, Davis)

Abstract: This paper discusses bio-inspired neuromorphic computing utilizing nanophotonic, nanoelectronic, and NEMS technologies integrated into reconfigurable 2D-3D integrated circuits as hierarchical neural networks. The goal is to achieve ≥1000x improvements in energy-per-operation compare to the state-of-the-art implementations of neural networks on Von-Neumann based computers. We combine nanophotonic and nanoelectronic technologies to build energy-efficient (~10 fJ/b) artificial spiking neurons with required functionality (spiking, integration, thresholding, reset). Photonic interconnects exploiting 2x2 NEMS-MZIs enables distance independent propagation of signal with weighted addition among the neurons as well as possibility of on-line learning capability. Using low-leakage nanophotonic and nanoelectronic devices, and NEMS, the static power consumption of the system can be decreased down to nearly zero. Realizing 2D-3D phothonic integrated circuit technologies, the proposed system can overcome the scalability limitations of current neuromorphic computing architectures.
Award: Best Poster Finalist (BP): yes

Poster: pdf
Two-page extended abstract: pdf

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