Search Results for author:
Found 7 papers, 3 papers with code
Dynamic Control Flow in Large-Scale Machine Learning
We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system.
MLIR: A Compiler Infrastructure for the End of Moore's Law
This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure.
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s
This paper presents a novel nearest neighbor search algorithm achieving TPU (Google Tensor Processing Unit) peak performance, outperforming state-of-the-art GPU algorithms with similar level of recall.
On Training a Neural Network to Explain Binaries
A major result of our work is a novel dataset evaluation method using the correlation between two distances on sample pairs: one distance in the embedding space of inputs and the other in the embedding space of outputs.
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters.
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TensorFlow: A system for large-scale machine learning
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
