In this video, Michael Garland discusses algorithmic design on GPUs with some emphasis on sparse matrix computation. Recorded at the 2010 Virtual Summer School of Computation Science and Engineering ...
This is a preview. Log in through your library . Abstract The sparsity constrained rank-one matrix approximation problem is a difficult mathematical optimization problem which arises in a wide array ...
Numenta Demonstrates 50x Speed Improvements on Deep Learning Networks Using Brain-Derived Algorithms
REDWOOD CITY, Calif.--(BUSINESS WIRE)--Using algorithms derived from its neuroscience research, Numenta announced today it has achieved dramatic performance improvements on inference tasks in deep ...
A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...
Abstract Multipoint polynomial evaluation and interpolation are fundamental for modern symbolic and numerical computing. The known algorithms solve both problems over any field of constants in nearly ...
Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Can artificial intelligence (AI) create its ...
Matrix multiplication is at the heart of many machine learning breakthroughs, and it just got faster—twice. Last week, DeepMind announced it discovered a more efficient way to perform matrix ...
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