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Found 5 papers, 1 papers with code
Detail-Preserving Pooling in Deep Networks
This is commonly referred to as pooling, and is applied to reduce the number of parameters, improve invariance to certain distortions, and increase the receptive field size.
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.
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.
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.
SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworks
While mainstream CPUs and GPUs have the "luxury" to have a wide spread user base in the open source community, less mainstream CPU, GPU or accelerator vendors need to put in a high effort to get their hardware supported by these frameworks.
