Fixed-Rate Compressed Floating-Point Arrays

By Peter Lindstrom. 2014

719 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Sheng Di
2. Franck Cappello
3. John V. Guttag

In Computer Vision and Pattern Recognition

1. Jonathan Frankle
2. John V. Guttag
3. Davis W. Blalock
4. Jose Javier Gonzalez Ortiz
5. Sheng Di
6. Franck Cappello
7. Zizhong Chen
8. Michael Carbin

In Signal Processing

1. Sheng Di
2. Franck Cappello
3. Jonathan Frankle

Citations in the corpus

• What is the State of Neural Network Pruning? (2020, background): “There is also widespread misuse of the term “compression ratio,” which the compression literature has long used to mean original size compressed size (Siedelmann et al., 2015; Zukowski et al., 2006; Zhao et al., 2015; Lindstrom, 2014; Ratanaworabhan et al., 2006; Blalock et al., 2018), but many prun”
• Fast Error-Bounded Lossy HPC Data Compression with SZ (2016, methodology): “The absolute error bound (denoted δ) will be set to a constant.”
• Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization (2017, background): “ZFP [13] uses an optimized orthogonal data transform that does not strongly rely on the data smoothness either; however, it requires an exponent/fixed-point alignment step, which might not respect the user error bound when the data value range is huge (as shown later in the paper).”