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Found 5 papers, 0 papers with code
SOL: Effortless Device Support for AI Frameworks without Source Code Changes
In this paper we explore how to provide hardware support in AI frameworks without changing the framework's source code in order to minimize maintenance overhead.
Towards Transparent Neural Network Acceleration
While the efficiency problem can be partially addressed with specialized hardware and its corresponding proprietary libraries, we believe that neural network acceleration should be transparent to the user and should support all hardware platforms and deep learning libraries.
BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism
Neural network frameworks such as PyTorch and TensorFlow are the workhorses of numerous machine learning applications ranging from object recognition to machine translation.
Representation Learning for Resource Usage Prediction
Creating a model of a computer system that can be used for tasks such as predicting future resource usage and detecting anomalies is a challenging problem.
Net2Vec: Deep Learning for the Network
We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network.
