Steven Eliuk
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View article: Continual Learning Long Short Term Memory
Continual Learning Long Short Term Memory Open
Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks. To address this problem, we propose a novel Continual Learning Long Short Term Memory (CL-L…
View article: Hoard: A Distributed Data Caching System to Accelerate Deep Learning Training on the Cloud
Hoard: A Distributed Data Caching System to Accelerate Deep Learning Training on the Cloud Open
Deep Learning system architects strive to design a balanced system where the computational accelerator -- FPGA, GPU, etc, is not starved for data. Feeding training data fast enough to effectively keep the accelerator utilization high is di…
View article: dMath: Distributed Linear Algebra for DL
dMath: Distributed Linear Algebra for DL Open
The paper presents a parallel math library, dMath, that demonstrates leading scaling when using intranode, internode, and hybrid-parallelism for deep learning (DL). dMath provides easy-to-use distributed primitives and a variety of domain-…
View article: dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures
dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures Open
A new scalable parallel math library, dMath, is presented in this paper that demonstrates leading scaling when using intranode, or internode, hybrid-parallelism for deep-learning. dMath provides easy-to-use distributed base primitives and …
View article: dMath: A Scalable Linear Algebra and Math Library for Heterogeneous\n GP-GPU Architectures
dMath: A Scalable Linear Algebra and Math Library for Heterogeneous\n GP-GPU Architectures Open
A new scalable parallel math library, dMath, is presented in this paper that\ndemonstrates leading scaling when using intranode, or internode,\nhybrid-parallelism for deep-learning. dMath provides easy-to-use distributed\nbase primitives a…