most citedTowards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.ET2024

TEGRA -- Scaling Up Terascale Graph Processing with Disaggregated Computing

William Shaddix, Mahyar Samani, Marjan Fariborz +3

Graphs are essential for representing relationships in various domains, driving modern AI applications such as graph analytics and neural networks across science, engineering, cybe…

cs.ET20241 cited

Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits

S. J. Ben Yoo, Luis El-Srouji, Suman Datta +11

The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match. For decades, reverse engineering the br…

physics.optics2024

Experimental Demonstration of Imperfection-Agnostic Local Learning Rules on Photonic Neural Networks with Mach-Zehnder Interferometric Meshes

Luis El Srouji, Mehmet Berkay On, Yun-Jhu Lee +2

Mach-Zehnder Interferometric meshes are attractive for low-loss photonic matrix multiplication but are challenging to program. Using least-squares optimization of directional deriv…

physics.optics2024

0.08 fF, 0.72 nA dark current, 91% Quantum Efficiency, 38 Gb/s Nano-photodetector on a 45 nm CMOS Silicon-Photonic Platform

Mingye Fu, S. J. Ben Yoo

We demonstrated a Germanium-on-Silicon photodetector utilizing an asymmetric-Fabry-Perot resonator with 0.08 fF capacitance. The measurements at 1315.5 nm show 0.72 nA (3.40 nA) da…

cs.NE2022

Scalable Nanophotonic-Electronic Spiking Neural Networks

Luis El Srouji, Yun-Jhu Lee, Mehmet Berkay On +2

Spiking neural networks (SNN) provide a new computational paradigm capable of highly parallelized, real-time processing. Photonic devices are ideal for the design of high-bandwidth…