# FQ-Conv: Fully Quantized Convolution for Efficient and Accurate Inference

arXiv: https://arxiv.org/abs/1912.09356 · HTML: https://mlsanity.com/paper/1912.09356/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference](https://mlsanity.com/paper/2306.05060/) (2023)
- [MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated Edge Inference](https://mlsanity.com/paper/2107.06960/) (2021)
- [Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images](https://mlsanity.com/paper/2203.02571/) (2022)
- [Constraint Guided Model Quantization of Neural Networks](https://mlsanity.com/paper/2409.20138/) (2024)
- [Optimizing DNN Inference on Multi-Accelerator SoCs at Training-time](https://mlsanity.com/paper/2409.18566/) (2024)

## Abstract (arXiv)

Deep neural networks (DNNs) can be made hardware-efficient by reducing the numerical precision of the weights and activations of the network and by improving the network's resilience to noise. However, this gain in efficiency often comes at the cost of significantly reduced accuracy. In this paper, we present a novel approach to quantizing convolutional neural network. The resulting networks perform all computations in low-precision, without requiring higher-precision BN and nonlinearities, while still being highly accurate. To achieve this result, we employ a novel quantization technique that learns to optimally quantize the weights and activations of the network during training. Additionally, to enhance training convergence we use a new training technique, called gradual quantization. We leverage the nonlinear and normalizing behavior of our quantization function to effectively remove the higher-precision nonlinearities and BN from the network. The resulting convolutional layers are fully quantized to low precision, from input to output, ideal for neural network accelerators on the edge. We demonstrate the potential of this approach on different datasets and networks, showing that ternary-weight CNNs with low-precision in- and outputs perform virtually on par with their full-precision equivalents. Finally, we analyze the influence of noise on the weights, activations and convolution outputs (multiply-accumulate, MAC) and propose a strategy to improve network performance under noisy conditions.