b'Wen-mei Hwu'

research

∙ 01/08/2022

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs

Dynamic parallelism on GPUs allows GPU threads to dynamically launch oth...

Mhd Ghaith Olabi, et al. ∙

research

∙ 11/10/2021

Graph Neural Network Training with Data Tiering

Graph Neural Networks (GNNs) have shown success in learning from graph-s...

Seung Won Min, et al. ∙

research

∙ 11/09/2021

MLHarness: A Scalable Benchmarking System for MLCommons

With the society's growing adoption of machine learning (ML) and deep le...

Yen-Hsiang Chang, et al. ∙

research

∙ 08/20/2021

Open Relation Modeling: Learning to Define Relations between Entities

Relations between entities can be represented by different instances, e....

Jie Huang, et al. ∙

research

∙ 05/27/2021

Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach

We propose to measure fine-grained domain relevance - the degree that a ...

Jie Huang, et al. ∙

research

∙ 04/27/2021

K-Clique Counting on GPUs

Counting k-cliques in a graph is an important problem in graph analysis ...

Mohammad Almasri, et al. ∙

research

∙ 03/04/2021

Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture

Graph Convolutional Networks (GCNs) are increasingly adopted in large-sc...

Seung Won Min, et al. ∙

research

∙ 01/20/2021

PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses

With the increasing adoption of graph neural networks (GNNs) in the mach...

Seung Won Min, et al. ∙

research

∙ 01/19/2021

Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19

COVID-19 has fundamentally disrupted the way we live. Government bodies,...

Vikram Sharma Mailthody, et al. ∙

research

∙ 12/28/2020

TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes

MPI derived datatypes are an abstraction that simplifies handling of non...

Carl Pearson, et al. ∙

research

∙ 11/23/2020

Interpretable Visual Reasoning via Induced Symbolic Space

We study the problem of concept induction in visual reasoning, i.e., ide...

Zhonghao Wang, et al. ∙

research

∙ 10/14/2020

Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices

High quality AI solutions require joint optimization of AI algorithms, s...

Cong Hao, et al. ∙

research

∙ 10/05/2020

Exploring Semantic Capacity of Terms

We introduce and study semantic capacity of terms. For example, the sema...

Jie Huang, et al. ∙

research

∙ 08/28/2020

DNNExplorer: A Framework for Modeling and Exploring a Novel Paradigm of FPGA-based DNN Accelerator

Existing FPGA-based DNN accelerators typically fall into two design para...

Xiaofan Zhang, et al. ∙

research

∙ 08/24/2020

Tearing Down the Memory Wall

We present a vision for the Erudite architecture that redefines the comp...

Zaid Qureshi, et al. ∙

research

∙ 07/28/2020

At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation

This paper presents GPU performance optimization and scaling results for...

Mert Hidayetoglu, et al. ∙

research

∙ 06/12/2020

EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal In GPUs

Modern analytics and recommendation systems are increasingly based on gr...

Seung Won Min, et al. ∙

research

∙ 05/06/2020

EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions

High quality AI solutions require joint optimization of AI algorithms an...

Yuhong Li, et al. ∙

research

∙ 04/02/2020

Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation

Learning segmentation from synthetic data and adapting to real data can ...

Zhonghao Wang, et al. ∙

research

∙ 03/18/2020

Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation

We consider the problem of unsupervised domain adaptation for semantic s...

Zhonghao Wang, et al. ∙

research

∙ 02/26/2020

DLSpec: A Deep Learning Task Exchange Specification

Deep Learning (DL) innovations are being introduced at a rapid pace. How...

Abdul Dakkak, et al. ∙

research

∙ 02/19/2020

MLModelScope: A Distributed Platform for Model Evaluation and Benchmarking at Scale

Machine Learning (ML) and Deep Learning (DL) innovations are being intro...

Abdul Dakkak, et al. ∙

research

∙ 12/24/2019

PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAM

The wide adoption of deep neural networks has been accompanied by ever-i...

Aayush Ankit, et al. ∙

research

∙ 11/19/2019

The Design and Implementation of a Scalable DL Benchmarking Platform

The current Deep Learning (DL) landscape is fast-paced and is rife with ...

Cheng Li, et al. ∙

research

∙ 11/18/2019

DLBricks: Composable Benchmark Generation toReduce Deep Learning Benchmarking Effort on CPUs

The past few years have seen a surge of applying Deep Learning (DL) mode...

Cheng Li, et al. ∙

research

∙ 11/18/2019

DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs

The past few years have seen a surge of applying Deep Learning (DL) mode...

Cheng Li, et al. ∙

research

∙ 11/18/2019

DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)

The past few years have seen a surge of applying Deep Learning (DL) mode...

Cheng Li, et al. ∙

research

∙ 11/18/2019

NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving

The rapidly growing demands for powerful AI algorithms in many applicati...

Cong Hao, et al. ∙

research

∙ 11/16/2019

Benanza: Automatic μBenchmark Generation to Compute "Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs

As Deep Learning (DL) models have been increasingly used in latency-sens...

Cheng Li, et al. ∙

research

∙ 11/16/2019

Benanza: Automatic uBenchmark Generation to Compute "Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs

As Deep Learning (DL) models have been increasingly used in latency-sens...

Cheng Li, et al. ∙

research

∙ 09/25/2019

PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space

Finding the right reviewers to assess the quality of conference submissi...

Omer Anjum, et al. ∙

research

∙ 09/20/2019

SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems

Developing object detection and tracking on resource-constrained embedde...

Xiaofan Zhang, et al. ∙

research

∙ 08/26/2019

SPGNet: Semantic Prediction Guidance for Scene Parsing

Multi-scale context module and single-stage encoder-decoder structure ar...

Bowen Cheng, et al. ∙

research

∙ 08/19/2019

Across-Stack Profiling and Characterization of Machine Learning Models on GPUs

The world sees a proliferation of machine learning/deep learning (ML) mo...

Cheng Li, et al. ∙

research

∙ 08/19/2019

XSP: Across-Stack Profiling and Analysis of Machine Learning Models on GPUs

There has been a rapid proliferation of machine learning/deep learning (...

Cheng Li, et al. ∙

research

∙ 08/04/2019

Analysis and Optimization of I/O Cache Coherency Strategies for SoC-FPGA Device

Unlike traditional PCIe-based FPGA accelerators, heterogeneous SoC-FPGA ...

Seung Won Min, et al. ∙

research

∙ 06/25/2019

SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection

Developing artificial intelligence (AI) at the edge is always challengin...

Xiaofan Zhang, et al. ∙

research

∙ 06/22/2019

A Retrospective Recount of Computer Architecture Research with a Data-Driven Study of Over Four Decades of ISCA Publications

This study began with a research project, called DISCvR, conducted at th...

Omer Anjum, et al. ∙

research

∙ 05/20/2019

A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices

Developing deep learning models for resource-constrained Internet-of-Thi...

Xiaofan Zhang, et al. ∙

research

∙ 04/29/2019

Challenges and Pitfalls of Reproducing Machine Learning Artifacts

An increasingly complex and diverse collection of Machine Learning(ML) m...

Cheng Li, et al. ∙

research

∙ 04/29/2019

Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking

An increasingly complex and diverse collection of Machine Learning (ML) ...

Cheng Li, et al. ∙

research

∙ 04/09/2019

FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge

While embedded FPGAs are attractive platforms for DNN acceleration on ed...

Cong Hao, et al. ∙

research

∙ 03/26/2019

Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus

Text style transfer rephrases a text from a source style (e.g., informal...

Hongyu Gong, et al. ∙

research

∙ 11/24/2018

MLModelScope: Evaluate and Measure ML Models within AI Pipelines

The current landscape of Machine Learning (ML) and Deep Learning (DL) is...

Abdul Dakkak, et al. ∙

research

∙ 11/24/2018

Accelerating Reduction and Scan Using Tensor Core Units

Driven by deep learning, there has been a surge of specialized processor...

Abdul Dakkak, et al. ∙

research

∙ 11/24/2018

TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

Deep neural networks (DNNs) have become core computation components with...

Abdul Dakkak, et al. ∙

research

∙ 09/18/2018

SCOPE: C3SR Systems Characterization and Benchmarking Framework

This report presents the design of the Scope infrastructure for extensib...

Carl Pearson, et al. ∙

Wen-mei Hwu

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