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Found 12 papers, 3 papers with code
Question-Answering System Extracts Information on Injection Drug Use from Clinical Progress Notes
Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity.
TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Training
Collective communications are an indispensable part of distributed training.
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
In this paper, we extend the open-source ASTRA-sim infrastructure and endow it with the capabilities to model state-of-the-art and emerging distributed training models and platforms.
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task
We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets.
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models
Distributed training is a solution to reduce DNN training time by splitting the task across multiple NPUs (e. g., GPU/TPU).
Exploring Multi-dimensional Hierarchical Network Topologies for Efficient Distributed Training of Trillion Parameter DL Models
High-performance distributed training platforms should leverage multi-dimensional hierarchical networks, which interconnect accelerators through different levels of the network, to dramatically reduce expensive NICs required for the scale-out network.
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations
To this end, and using those established techniques, we first developed an experimental frame-work for author detection and input perturbations.
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures
During the last two years, the goal of many researchers has been to squeeze the last bit of performance out of HPC system for AI tasks.
K-TanH: Efficient TanH For Deep Learning
We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning.
High Performance Scalable FPGA Accelerator for Deep Neural Networks
Low-precision is the first order knob for achieving higher Artificial Intelligence Operations (AI-TOPS).
Mixed Precision Training With 8-bit Floating Point
Reduced precision computation for deep neural networks is one of the key areas addressing the widening compute gap driven by an exponential growth in model size.
A Study of BFLOAT16 for Deep Learning Training
In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16.
