# AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch

arXiv: https://arxiv.org/abs/2203.04071 · HTML: https://mlsanity.com/paper/2203.04071/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [One Epoch Is All You Need](https://mlsanity.com/paper/1906.06669/) (2019)
- [ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining](https://mlsanity.com/paper/1907.07229/) (2019)
- [TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU](https://mlsanity.com/paper/2002.09481/) (2020)
- [Control Variate Approximation for DNN Accelerators](https://mlsanity.com/paper/2102.09642/) (2021)
- [Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks](https://mlsanity.com/paper/1605.06402/) (2016)
- [Mechanism to Mitigate AVX-Induced Frequency Reduction](https://mlsanity.com/paper/1901.04982/) (2018)

## Built on by (most cited first, 11 of 11)

- [Approximate Computing and the Efficient Machine Learning Expedition](https://mlsanity.com/paper/2210.00497/) (2022)
- [TransAxx: Efficient Transformers with Approximate Computing](https://mlsanity.com/paper/2402.07545/) (2024)
- [AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Engines](https://mlsanity.com/paper/2512.15946/) (2025)
- [AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators](https://mlsanity.com/paper/2403.02936/) (2024)
- [USEFUSE: Uniform Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks](https://mlsanity.com/paper/2412.13724/) (2024)
- [Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers](https://mlsanity.com/paper/2404.11665/) (2024)
- [FAMES: Fast Approximate Multiplier Substitution for Mixed-Precision Quantized DNNs--Down to 2 Bits!](https://mlsanity.com/paper/2411.18055/) (2024)
- [Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays](https://mlsanity.com/paper/2503.16583/) (2025)
- [Gradient Estimation Methods of Approximate Multipliers for High-Accuracy Retraining of Deep Learning Models](https://mlsanity.com/paper/2509.10519/) (2025)
- [AxMoE: Characterizing the Impact of Approximate Multipliers on Mixture-of-Experts DNN Architectures](https://mlsanity.com/paper/2605.04754/) (2026)
- [FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retraining](https://mlsanity.com/paper/2609.17730/) (2026)

## Abstract (arXiv)

Current state-of-the-art employs approximate multipliers to address the highly increased power demands of DNN accelerators. However, evaluating the accuracy of approximate DNNs is cumbersome due to the lack of adequate support for approximate arithmetic in DNN frameworks. We address this inefficiency by presenting AdaPT, a fast emulation framework that extends PyTorch to support approximate inference as well as approximation-aware retraining. AdaPT can be seamlessly deployed and is compatible with the most DNNs. We evaluate the framework on several DNN models and application fields including CNNs, LSTMs, and GANs for a number of approximate multipliers with distinct bitwidth values. The results show substantial error recovery from approximate re-training and reduced inference time up to 53.9x with respect to the baseline approximate implementation.