Wen-mei Hwu

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

Modern analytics and recommendation systems are increasingly based on gr...
06/12/2020 ∙ by Seung Won Min, et al. ∙ 0 ∙ share

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

High quality AI solutions require joint optimization of AI algorithms an...
05/06/2020 ∙ by Yuhong Li, et al. ∙ 0 ∙ share

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

Learning segmentation from synthetic data and adapting to real data can ...
04/02/2020 ∙ by Zhonghao Wang, et al. ∙ 3 ∙ share

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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...
03/18/2020 ∙ by Zhonghao Wang, et al. ∙ 2 ∙ share

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DLSpec: A Deep Learning Task Exchange Specification

Deep Learning (DL) innovations are being introduced at a rapid pace. How...
02/26/2020 ∙ by Abdul Dakkak, et al. ∙ 0 ∙ share

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

Machine Learning (ML) and Deep Learning (DL) innovations are being intro...
02/19/2020 ∙ by Abdul Dakkak, et al. ∙ 15 ∙ share

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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...
12/24/2019 ∙ by Aayush Ankit, et al. ∙ 0 ∙ share

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The Design and Implementation of a Scalable DL Benchmarking Platform

The current Deep Learning (DL) landscape is fast-paced and is rife with ...
11/19/2019 ∙ by Cheng Li, et al. ∙ 55 ∙ share

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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...
11/18/2019 ∙ by Cheng Li, et al. ∙ 23 ∙ share

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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...
11/18/2019 ∙ by Cheng Li, et al. ∙ 11 ∙ share

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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...
11/18/2019 ∙ by Cheng Li, et al. ∙ 0 ∙ share

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

The rapidly growing demands for powerful AI algorithms in many applicati...
11/18/2019 ∙ by Cong Hao, et al. ∙ 24 ∙ share

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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...
11/16/2019 ∙ by Cheng Li, et al. ∙ 12 ∙ share

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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...
11/16/2019 ∙ by Cheng Li, et al. ∙ 5 ∙ share

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

Finding the right reviewers to assess the quality of conference submissi...
09/25/2019 ∙ by Omer Anjum, et al. ∙ 0 ∙ share

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

Developing object detection and tracking on resource-constrained embedde...
09/20/2019 ∙ by Xiaofan Zhang, et al. ∙ 41 ∙ share

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SPGNet: Semantic Prediction Guidance for Scene Parsing

Multi-scale context module and single-stage encoder-decoder structure ar...
08/26/2019 ∙ by Bowen Cheng, et al. ∙ 16 ∙ share

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

The world sees a proliferation of machine learning/deep learning (ML) mo...
08/19/2019 ∙ by Cheng Li, et al. ∙ 2 ∙ share

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

There has been a rapid proliferation of machine learning/deep learning (...
08/19/2019 ∙ by Cheng Li, et al. ∙ 0 ∙ share

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

Unlike traditional PCIe-based FPGA accelerators, heterogeneous SoC-FPGA ...
08/04/2019 ∙ by Seung Won Min, et al. ∙ 0 ∙ share

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

Developing artificial intelligence (AI) at the edge is always challengin...
06/25/2019 ∙ by Xiaofan Zhang, et al. ∙ 1 ∙ share

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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...
06/22/2019 ∙ by Omer Anjum, et al. ∙ 0 ∙ share

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

Developing deep learning models for resource-constrained Internet-of-Thi...
05/20/2019 ∙ by Xiaofan Zhang, et al. ∙ 0 ∙ share

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Challenges and Pitfalls of Reproducing Machine Learning Artifacts

An increasingly complex and diverse collection of Machine Learning(ML) m...
04/29/2019 ∙ by Cheng Li, et al. ∙ 6 ∙ share

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Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking

An increasingly complex and diverse collection of Machine Learning (ML) ...
04/29/2019 ∙ by Cheng Li, et al. ∙ 0 ∙ share

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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...
04/09/2019 ∙ by Cong Hao, et al. ∙ 0 ∙ share

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Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus

Text style transfer rephrases a text from a source style (e.g., informal...
03/26/2019 ∙ by Hongyu Gong, et al. ∙ 0 ∙ share

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MLModelScope: Evaluate and Measure ML Models within AI Pipelines

The current landscape of Machine Learning (ML) and Deep Learning (DL) is...
11/24/2018 ∙ by Abdul Dakkak, et al. ∙ 0 ∙ share

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Accelerating Reduction and Scan Using Tensor Core Units

Driven by deep learning, there has been a surge of specialized processor...
11/24/2018 ∙ by Abdul Dakkak, et al. ∙ 0 ∙ share

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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...
11/24/2018 ∙ by Abdul Dakkak, et al. ∙ 0 ∙ share

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SCOPE: C3SR Systems Characterization and Benchmarking Framework

This report presents the design of the Scope infrastructure for extensib...
09/18/2018 ∙ by Carl Pearson, et al. ∙ 0 ∙ share

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Wen-mei Hwu

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