output
20172021
most citedInverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning

37 citations

5 papers

physics.comp-ph202137 cited

Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning

Sean Hooten, Raymond G. Beausoleil, Thomas Van Vaerenbergh

We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic…

astro-ph.SR20207 cited

ALMA reveals the coherence of the magnetic field geometry in OH 231.8+4.2

L. Sabin, R. Sahai, W. H. T. Vlemmings +7

In a continuing effort to investigate the role of magnetic fields in evolved low and intermediate mass stars (principally regarding the shaping of their envelopes), we present new…

cs.DC201913 cited

Analyzing the Impact of GDPR on Storage Systems

Aashaka Shah, Vinay Banakar, Supreeth Shastri +2

The recently introduced General Data Protection Regulation (GDPR) is forcing several companies to make significant changes to their systems to achieve compliance. Motivated by the…

cs.ET201925 cited

PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference

Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti +8

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications, overcoming the fundamental energy efficiency limitations of digital logic. They have be…

cond-mat.mtrl-sci201734 cited

Spatially uniform resistance switching of low current, high endurance titanium-niobium-oxide memristors

Suhas Kumar, Noraica Davila, Ziwen Wang +6

We analyzed micrometer-scale titanium-niobium-oxide prototype memristors, which exhibited low write-power (<3 μW) and energy (<200 fJ/bit/μm2), low read-power (~nW), and high endur…