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Keyword

low-precision arithmetic

Description: Numerical computation using representations with fewer bits or less precision than standard formats, often to reduce computational cost in scientific and machine-learning applications.
OpenAlex ID: keywords/low-precision-arithmetic
Arithmetic coding revisited
1998 · Alistair Moffat, Radford M. Neal, et al. · ACM Transactions on Information Systems · 471
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Training deep neural networks with low precision multiplications
2014 · Courbariaux, Matthieu, Bengio, Yoshua, et al. · arXiv (Cornell University) · 407
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Convolutional Neural Networks using Logarithmic Data Representation
2016 · Miyashita, Daisuke, Lee, Edward H., et al. · arXiv (Cornell University) · 345
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Training Deep Neural Networks with 8-bit Floating Point Numbers
2018 · Wang, Naigang, Choi, Jungwook, et al. · arXiv (Cornell University) · 299
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Arithmetic coding for data compression
1994 · P.G. Howard, J.S. Vitter · Proceedings of the IEEE · 274
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
2017 · Mishra, Asit, Marr, Debbie · arXiv (Cornell University) · 257
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Embedded Online Optimization for Model Predictive Control at Megahertz Rates
2014 · Juan L. Jerez, Paul J. Goulart, et al. · IEEE Transactions on Automatic Control · 257
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Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks
2018 · Philipp Gysel, Jon Pimentel, et al. · IEEE Transactions on Neural Networks and Learning Systems · 250
Finite precision error analysis of neural network hardware implementations
1993 · J.L. Holt, J.-N. Hwang · IEEE Transactions on Computers · 230
A Massively Parallel Coprocessor for Convolutional Neural Networks
2009 · Murugan Sankaradas, Venkata Jakkula, et al. · IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP) · 226
A Review of Binarized Neural Networks
2019 · Taylor Simons, Dah-Jye Lee · Electronics · 219
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Fast matrix multiplies using graphics hardware
2001 · E. Scott Larsen, David McAllister · Proceedings - Supercomputing · 216
High-Performance FPGA-Based CNN Accelerator With Block-Floating-Point Arithmetic
2019 · Xiaocong Lian, Zhenyu Liu, et al. · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · 161
Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?
2019 · Shilin Zhu, Xin Dong, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 153
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Minimum energy quantized neural networks
2017 · Bert Moons, Koen Goetschalckx, et al. · 2018 52nd Asilomar Conference on Signals, Systems, and Computers · 152
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Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
2021 · Jacob R. Stevens, Rangharajan Venkatesan, et al. · Proceedings - ACM IEEE Design Automation Conference · 147
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Training Quantized Nets: A Deeper Understanding
2017 · Li, Hao, De, Soham, et al. · arXiv (Cornell University) · 142
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Supercomputer Investigations of a Chaotic Encryption Algorithm
1991 · Daniel D. Wheeler, Robert A. J. Matthews · Cryptologia · 126
Deep Positron: A Deep Neural Network Using the Posit Number System
2019 · Zachariah Carmichael, Hamed F. Langroudi, et al. · Design, Automation, and Test in Europe · 123
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Accelerating Deep Convolutional Networks using low-precision and sparsity
2017 · Ganesh Venkatesh, Eriko Nurvitadhi, et al. · IEEE International Conference on Acoustics Speech and Signal Processing · 123
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Neural inference at the frontier of energy, space, and time
2023 · Dharmendra S. Modha, Filipp Akopyan, et al. · Science · 114
Introduction to Arithmetic Coding -- Theory and Practice
2023 · Said, Amir · arXiv (Cornell University) · 108
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A Stochastic Computational Multi-Layer Perceptron with Backward Propagation
2018 · Yidong Liu, Siting Liu, et al. · IEEE Transactions on Computers · 108
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms
2015 · De Sa, Christopher, Zhang, Ce, et al. · PubMed · 107
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9.1 A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling
2021 · Ankur Agrawal, Sae Kyu Lee, et al. · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · 106
Works count: 1,614
Citation count: 16,110
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