# ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers

arXiv: https://arxiv.org/abs/2404.08002 · HTML: https://mlsanity.com/paper/2404.08002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 21 of 21)

- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search](https://mlsanity.com/paper/1907.05737/) (2019)
- [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://mlsanity.com/paper/2006.02903/) (2020)
- [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://mlsanity.com/paper/1904.12760/) (2019)
- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [SGAS: Sequential Greedy Architecture Search](https://mlsanity.com/paper/1912.00195/) (2019)
- [DSNAS: Direct Neural Architecture Search without Parameter Retraining](https://mlsanity.com/paper/2002.09128/) (2020)
- [A Genetic Programming Approach to Designing Convolutional Neural Network Architectures](https://mlsanity.com/paper/1704.00764/) (2017)
- [Ensemble Knowledge Distillation for Learning Improved and Efficient Networks](https://mlsanity.com/paper/1909.08097/) (2019)
- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [XNAS: Neural Architecture Search with Expert Advice](https://mlsanity.com/paper/1906.08031/) (2019)
- [Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search](https://mlsanity.com/paper/2006.07593/) (2020)

## Built on by (most cited first, 1 of 1)

- [HAWX: A Hardware-Aware FrameWork for Fast and Scalable ApproXimation of DNNs](https://mlsanity.com/paper/2602.16336/) (2026)

## Abstract (arXiv)

Integrating the principles of approximate computing into the design of hardware-aware deep neural networks (DNN) has led to DNNs implementations showing good output quality and highly optimized hardware parameters such as low latency or inference energy. In this work, we present ApproxDARTS, a neural architecture search (NAS) method enabling the popular differentiable neural architecture search method called DARTS to exploit approximate multipliers and thus reduce the power consumption of generated neural networks. We showed on the CIFAR-10 data set that the ApproxDARTS is able to perform a complete architecture search within less than 10 GPU hours and produce competitive convolutional neural networks (CNN) containing approximate multipliers in convolutional layers. For example, ApproxDARTS created a CNN showing an energy consumption reduction of (a) 53.84% in the arithmetic operations of the inference phase compared to the CNN utilizing the native 32-bit floating-point multipliers and (b) 5.97% compared to the CNN utilizing the exact 8-bit fixed-point multipliers, in both cases with a negligible accuracy drop. Moreover, the ApproxDARTS is 2.3× faster than a similar but evolutionary algorithm-based method called EvoApproxNAS.