# AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference

arXiv: https://arxiv.org/abs/1805.08309 · HTML: https://mlsanity.com/paper/1805.08309/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 8 of 8)

- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [TransAxx: Efficient Transformers with Approximate Computing](https://mlsanity.com/paper/2402.07545/) (2024)
- [R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors](https://mlsanity.com/paper/2107.03096/) (2021)
- [Winograd Convolution: A Perspective from Fault Tolerance](https://mlsanity.com/paper/2202.08675/) (2022)
- [Combining Gradients and Probabilities for Heterogeneous Approximation of Neural Networks](https://mlsanity.com/paper/2208.07265/) (2022)
- [DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training and Inference](https://mlsanity.com/paper/2305.07376/) (2023)
- [Cross-Layer Optimization for Fault-Tolerant Deep Learning](https://mlsanity.com/paper/2312.13754/) (2023)
- [Gradient Estimation Methods of Approximate Multipliers for High-Accuracy Retraining of Deep Learning Models](https://mlsanity.com/paper/2509.10519/) (2025)

## Abstract (arXiv)

The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference. Conventional approximate computing focuses on balancing the efficiency-accuracy trade-off for existing pre-trained networks, which can lead to suboptimal solutions. In this paper, we propose AxTrain, a hardware-oriented training framework to facilitate approximate computing for NN inference. Specifically, AxTrain leverages the synergy between two orthogonal methods---one actively searches for a network parameters distribution with high error tolerance, and the other passively learns resilient weights by numerically incorporating the noise distributions of the approximate hardware in the forward pass during the training phase. Experimental results from various datasets with near-threshold computing and approximation multiplication strategies demonstrate AxTrain's ability to obtain resilient neural network parameters and system energy efficiency improvement.