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Found 4 papers, 1 papers with code
Compiling Neural Networks for a Computational Memory Accelerator
Computational memory (CM) is a promising approach for accelerating inference on neural networks (NN) by using enhanced memories that, in addition to storing data, allow computations on them.
Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle
To address this, we propose Chicle, a new elastic distributed training framework which exploits the nature of machine learning algorithms to implement elasticity and load balancing without micro-tasks.
Elastic CoCoA: Scaling In to Improve Convergence
In this paper we experimentally analyze the convergence behavior of CoCoA and show, that the number of workers required to achieve the highest convergence rate at any point in time, changes over the course of the training.
Parallel training of linear models without compromising convergence
The combined set of optimizations result in a consistent bottom line speedup in convergence of up to 12x compared to the initial asynchronous parallel training algorithm and up to 42x, compared to state of the art implementations (scikit-learn and h2o) on a range of multi-core CPU architectures.
