Ergebnisse für: Block Sparsity

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Buch Cover Minimizing Data Movement and Parameter Count Across the Machine Learning Stack
Minimizing Data Movement and Parameter Count Across the Machine Learning Stack
Andrew Sabot
Springer International Publishing
42.79 € · Hardcover
Structured Sparsity Execution Gather Scatter GPU Block Distillation Low-rank SVD Fine-tuning Sparse Attention Kernels Tensor Contraction Optimization Matrix Multiplication Blocking Sparse Inference Scheduling Triton/CUDA Kernel Optimization
This book provides a focused, research-forward guide to making large AI models efficient in practice and also presents an array of novel techniques to reduce memory footprint, accelerate computation, and improve overall hardware utilization. The author demonstrates that substantial efficiency gains ...
Buch Cover Minimizing Data Movement and Parameter Count Across the Machine Learning Stack
Minimizing Data Movement and Parameter Count Across the Machine Learning Stack
Andrew Sabot
Springer International Publishing
42.79 € · eBook
Structured Sparsity Execution Gather Scatter GPU Block Distillation Low-rank SVD Fine-tuning Sparse Attention Kernels Tensor Contraction Optimization Matrix Multiplication Blocking Sparse Inference Scheduling Triton/CUDA Kernel Optimization
This book provides a focused, research-forward guide to making large AI models efficient in practice and also presents an array of novel techniques to reduce memory footprint, accelerate computation, and improve overall hardware utilization. The author demonstrates that substantial efficiency gains ...
Buch Cover Structured Sparse Signal Recovery in General Hilbert Spaces
Structured Sparse Signal Recovery in General Hilbert Spaces
Graeme Pope
Hartung-Gorre
64 € · Buch
Basis Pursuit Block Sparsity Coherence Compressed sensing Deterministic recovery guarantees Nuclear Norm Minimization Probabilistic recovery guarantees Sparse noise Sparse signal recovery Uncertainty relations l1 minimization
Data analysis is fundamental to many parts of modern society. This work examines how we can use sparsity to extract meaning and structure from complex data sets. We begin by establishing a general framework for imposing a model on the sparse representation of a signal. This not only encompasses m...
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