mlsanityData to 7 Oct 2026

Neural Network Distiller: A Python Package For DNN Compression Research

arXiv 1910.12232 ↗First posted 27 Oct 2019cs.LG stat.MLcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2019.

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Citing papers per year: 2021: 7; 2022: 2; 2023: 3.

12 papers.

  1. 2021Auto-Split: A General Framework of Collaborative Edge-Cloud AIcited by 29
  2. 2021Dual-side Sparse Tensor Corecited by 27
  3. 2021NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMMcited by 19
  4. 2021Shrinking Bigfoot: Reducing wav2vec 2.0 footprintcited by 16
  5. 2023JaxPruner: A concise library for sparsity researchcited by 13
  6. 2022Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resourcescited by 5
  7. 2021Robustness in Compressed Neural Networks for Object Detectioncited by 4
  8. 2022Distilled Neural Networks for Efficient Learning to Rankcited by 4
  9. 2021Benchmarking Quantized Neural Networks on FPGAs with FINNcited by 2
  10. 2021Finding Deviated Behaviors of the Compressed DNN Models for Image Classificationscited by 1
  11. 2023Evaluation Metrics for DNNs Compressioncited by 0
  12. 2023Structured Network Pruning by Measuring Filter-wise Interactionscited by 0