Explanipedia

Over 100% Light Extraction Enhancement of Organic Light Emitting Diodes Using Flat Moire Micro-Lens Array Fabricated by Double Nanoimprint Lithography Over a Large Area Open

Ji Qi, Wei Ding, Qi Zhang, Yuxuan Wang, Hao Chen , et al. · 2023

To improve the light extraction efficiency of organic light emitting diodes (OLEDs), we developed a novel substrate, i.e., a metamaterial based flat Moire micro-lens array formed using double nanoimprint, termed Mlens-array, consisting of …

Enhancing Light Extraction of Organic Light Emitting Diodes by Deep-Groove High-index Dielectric Nanomesh Using Large-area Nanoimprint Open

Ji Qi, Wei Ding, Qi Zhang, Yuxuan Wang, Hao Chen , et al. · 2023

To solve the conventional conflict between maintaining good charge transport property and achieving high light extraction efficiency when using micro/nanostructure patterned substrates to extract light from organic light emitting diodes (O…

Significant Light Extraction and Power Efficiency Enhancement of Organic Light Emitting Diodes by Subwavelength Dielectric-Nanomesh Using Large-area Nanoimprint Open

Ji Qi, Wei Ding, Qi Zhang, Yuxuan Wang, Hao Chen , et al. · 2023

To improve the power efficiency of light emitting diodes (OLEDs), we developed a novel OLED structure, termed Dielectric-Nanomesh OLED (DNM-OLED), fabricated by large-area nanoimprint lithography (NIL). A dielectric-nanomesh substrate with…

Nanochannel arrays and their preparation and use for high throughput macromolecular analysis Open

Robert H. Austin, Zhaoning Yu, Jonas O. Tegenfeldt, Stephen Y. Chou, Han Cao · 2023

Nanochannel arrays that enable high-throughput macromolecular analysis are disclosed. Also disclosed are methods of preparing nanochannel arrays and nanofluidic chips. Methods of analyzing macromolecules, such as entire strands of genomic …

Compilation of dynamic sparse tensor algebra Open

Stephen Y. Chou, Saman Amarasinghe · 2022

Many applications, from social network graph analytics to control flow analysis, compute on sparse data that evolves over the course of program execution. Such data can be represented as dynamic sparse tensors and efficiently stored in for…

Dynamic Sparse Tensor Algebra Compilation Open

Stephen Y. Chou, Saman Amarasinghe · 2021

This paper shows how to generate efficient tensor algebra code that compute on dynamic sparse tensors, which have sparsity structures that evolve over time. We propose a language for precisely specifying recursive, pointer-based data struc…

Compilation of sparse array programming models Open

Rawn Henry, Olivia Hsu, Rohan Yadav, Stephen Y. Chou, Kunle Olukotun , et al. · 2021

This paper shows how to compile sparse array programming languages. A sparse array programming language is an array programming language that supports element-wise application, reduction, and broadcasting of arbitrary functions over dense …

An Attempt to Generate Code for Symmetric Tensor Computations Open

Jessica Shi, Stephen Y. Chou, Fredrik Kjølstad, Saman Amarasinghe · 2021

This document describes an attempt to develop a compiler-based approach for computations with symmetric tensors. Given a computation and the symmetries of its input tensors, we derive formulas for random access under a storage scheme that …

A sparse iteration space transformation framework for sparse tensor algebra Open

Ryan Senanayake, Changwan Hong, Ziheng Wang, Amalee Wilson, Stephen Y. Chou , et al. · 2020

We address the problem of optimizing sparse tensor algebra in a compiler and show how to define standard loop transformations---split, collapse, and reorder---on sparse iteration spaces. The key idea is to track the transformation function…

Automatic generation of efficient sparse tensor format conversion routines Open

Stephen Y. Chou, Fredrik Kjølstad, Saman Amarasinghe · 2020

This paper shows how to generate code that efficiently converts sparse tensors between disparate storage formats (data layouts) such as CSR, DIA, ELL, and many others. We decompose sparse tensor conversion into three logical phases: coordi…

Format abstraction for sparse tensor algebra compilers Open

Stephen Y. Chou, Fredrik Kjølstad, Saman Amarasinghe · 2018

This paper shows how to build a sparse tensor algebra compiler that is agnostic to tensor formats (data layouts). We develop an interface that describes formats in terms of their capabilities and properties, and show how to build a modular…

Unified Sparse Formats for Tensor Algebra Compilers. Open

Stephen Y. Chou, Fredrik Kjølstad, Saman Amarasinghe · 2018

The Tensor Algebra Compiler Open

Fredrik Kjølstad, Shoaib Kamil, Stephen Y. Chou, David Lugato, Saman Amarasinghe · 2017

Tensor and linear algebra is pervasive in data analytics and the physical sciences. Often the tensors, matrices or even vectors are sparse. Computing expressions involving a mix of sparse and dense tensors, matrices and vectors requires wr…

The tensor algebra compiler Open

Fredrik Kjølstad, Shoaib Kamil, Stephen Y. Chou, David Lugato, Saman Amarasinghe · 2017

Tensor algebra is a powerful tool with applications in machine learning, data analytics, engineering and the physical sciences. Tensors are often sparse and compound operations must frequently be computed in a single kernel for performance…

Foreword Open

Stephen Y. Chou · 2015

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