P50: Energy-Efficient and Scalable Bio-Inspired
Nanophotonic Computing
SessionPoster Reception
Authors
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionThis 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.
