Neural Network Distiller: A Python Package For DNN Compression Research
arXiv:1910.12232
Abstract
This paper presents the philosophy, design and feature-set of Neural Network Distiller, an open-source Python package for DNN compression research. Distiller is a library of DNN compression algorithms implementations, with tools, tutorials and sample applications for various learning tasks. Its target users are both engineers and researchers, and the rich content is complemented by a design-for-extensibility to facilitate new research. Distiller is open-source and is available on Github at https://github.com/NervanaSystems/distiller.
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Cited by in corpus (12)
- Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
- Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
- Robust Quantization: One Model to Rule Them All
- Distilled Neural Networks for Efficient Learning to Rank
- Benchmarking Quantized Neural Networks on FPGAs with FINN
- A flexible, extensible software framework for model compression based on the LC algorithm
- Robustness in Compressed Neural Networks for Object Detection
- Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA
- Practical Encrypted Computing for IoT Clients
- Self-Supervised GAN Compression
- Dual-side Sparse Tensor Core
- Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge