83 citations · 613 across the 55 of their papers we have counts for
14 papers · 1 filter
TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes
Carl Pearson, Kun Wu, I-Hsin Chung +2
MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications. These datatypes are recursively constructed at runtime from primitive…
Interpretable Visual Reasoning via Induced Symbolic Space
Zhonghao Wang, Kai Wang, Mo Yu +4
We study the problem of concept induction in visual reasoning, i.e., identifying concepts and their hierarchical relationships from question-answer pairs associated with images; an…
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices
Cong Hao, Yao Chen, Xiaofan Zhang +4
High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators. To improve the overall solution quality…
Exploring Semantic Capacity of Terms
Jie Huang, Zilong Wang, Kevin Chen-Chuan Chang +2
We introduce and study semantic capacity of terms. For example, the semantic capacity of artificial intelligence is higher than that of linear regression since artificial intellige…
Petascale XCT: 3D Image Reconstruction with Hierarchical Communications on Multi-GPU Nodes
Mert Hidayetoglu, Tekin Bicer, Simon Garcia de Gonzalo +6
X-ray computed tomography is a commonly used technique for noninvasive imaging at synchrotron facilities. Iterative tomographic reconstruction algorithms are often preferred for re…
Tearing Down the Memory Wall
Zaid Qureshi, Vikram Sharma Mailthody, Seung Won Min +3
We present a vision for the Erudite architecture that redefines the compute and memory abstractions such that memory bandwidth and capacity become first-class citizens along with c…
DNNExplorer: A Framework for Modeling and Exploring a Novel Paradigm of FPGA-based DNN Accelerator
Xiaofan Zhang, Hanchen Ye, Junsong Wang +4
Existing FPGA-based DNN accelerators typically fall into two design paradigms. Either they adopt a generic reusable architecture to support different DNN networks but leave some pe…
At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation
Mert Hidayetoglu, Carl Pearson, Vikram Sharma Mailthody +4
This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020. Demands for network quality have increas…
EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal In GPUs
Seung Won Min, Vikram Sharma Mailthody, Zaid Qureshi +3
Modern analytics and recommendation systems are increasingly based on graph data that capture the relations between entities being analyzed. Practical graphs come in huge sizes, of…
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions
Yuhong Li, Cong Hao, Xiaofan Zhang +5
High quality AI solutions require joint optimization of AI algorithms and their hardware implementations. In this work, we are the first to propose a fully simultaneous, efficient…
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation
Zhonghao Wang, Yunchao Wei, Rogerior Feris +4
Learning segmentation from synthetic data and adapting to real data can significantly relieve human efforts in labelling pixel-level masks. A key challenge of this task is how to a…
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
Zhonghao Wang, Mo Yu, Yunchao Wei +5
We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (rea…
DLSpec: A Deep Learning Task Exchange Specification
Abdul Dakkak, Cheng Li, Jinjun Xiong +1
Deep Learning (DL) innovations are being introduced at a rapid pace. However, the current lack of standard specification of DL tasks makes sharing, running, reproducing, and compar…
MLModelScope: A Distributed Platform for Model Evaluation and Benchmarking at Scale
Abdul Dakkak, Cheng Li, Jinjun Xiong +1
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them. The complicated proc…