# Faster Convolution Inference Through Using Pre-Calculated Lookup Tables

arXiv: https://arxiv.org/abs/2104.01681 · HTML: https://mlsanity.com/paper/2104.01681/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 74 references that are in the map.
- First posted on arXiv 4 Apr 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights](https://mlsanity.com/paper/1702.03044/) (2017)
- [Fast Training of Convolutional Networks through FFTs](https://mlsanity.com/paper/1312.5851/) (2013)
- [Towards Unified INT8 Training for Convolutional Neural Network](https://mlsanity.com/paper/1912.12607/) (2019)
- [EasyQuant: Post-training Quantization via Scale Optimization](https://mlsanity.com/paper/2006.16669/) (2020)
- [Flattened Convolutional Neural Networks for Feedforward Acceleration](https://mlsanity.com/paper/1412.5474/) (2014)
- [Very Efficient Training of Convolutional Neural Networks using Fast Fourier Transform and Overlap-and-Add](https://mlsanity.com/paper/1601.06815/) (2016)
- [Origami: A 803 GOp/s/W Convolutional Network Accelerator](https://mlsanity.com/paper/1512.04295/) (2015)
- [Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks](https://mlsanity.com/paper/1910.09455/) (2019)
- [Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions](https://mlsanity.com/paper/2003.12621/) (2020)
- [Precision Scaling of Neural Networks for Efficient Audio Processing](https://mlsanity.com/paper/1712.01340/) (2017)
- [Derivation and Analysis of Fast Bilinear Algorithms for Convolution](https://mlsanity.com/paper/1910.13367/) (2019)
- [Complexity Leads to Simplicity: A Consensus Layer V Pyramidal Neuron Can Sustain Interpulse-Interval Coding](https://mlsanity.com/paper/1609.08213/) (2016)
- [Towards a New Interpretation of Separable Convolutions](https://mlsanity.com/paper/1701.04489/) (2017)

## Abstract (arXiv)

Low-cardinality activations permit an algorithm based on fetching the inference values from pre-calculated lookup tables instead of calculating them every time. This algorithm can have extensions, some of which offer abilities beyond those of the currently used algorithms. It also allows for a simpler and more effective CNN-specialized hardware.