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05/15/2023
Question-Answering System Extracts Information on Injection Drug Use from Clinical Progress Notes
Injection drug use (IDU) is a dangerous health behavior that increases m...
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04/11/2023
TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Training
Collective communications are an indispensable part of distributed train...
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03/24/2023
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
As deep learning models and input data are scaling at an unprecedented r...
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02/26/2022
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task
Motivation: Biomedical machine reading comprehension (biomedical-MRC) ai...
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10/09/2021
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models
The continuous growth in both size and training data for modern Deep Neu...
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09/24/2021
Exploring Multi-dimensional Hierarchical Network Topologies for Efficient Distributed Training of Trillion Parameter DL Models
Deep Neural Networks have gained significant attraction due to their wid...
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03/18/2021
Extending Sparse Tensor Accelerators to Support Multiple Compression Formats
Sparsity, which occurs in both scientific applications and Deep Learning...
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02/23/2021
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations
Authorship analysis is an important subject in the field of natural lang...
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06/30/2020
Efficient Communication Acceleration for Next-Gen Scale-up Deep Learning Training Platforms
Deep Learning (DL) training platforms are built by interconnecting multi...
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06/30/2020
Efficient Communication Acceleration for Next-GenScale-up Deep Learning Training Platforms
Deep Learning (DL) training platforms are built by interconnecting multi...
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05/10/2020
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures
During the last two years, the goal of many researchers has been to sque...
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09/17/2019
K-TanH: Hardware Efficient Activations For Deep Learning
We propose K-TanH, a novel, highly accurate, hardware efficient approxim...
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08/29/2019
High Performance Scalable FPGA Accelerator for Deep Neural Networks
Low-precision is the first order knob for achieving higher Artificial In...
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05/29/2019
Mixed Precision Training With 8-bit Floating Point
Reduced precision computation for deep neural networks is one of the key...
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05/29/2019
