mlsanityData to 7 Oct 2026

AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch

arXiv 2203.04071 ↗First posted 8 Mar 2022cs.LG cs.ARcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

10 of 23 references.

References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
  4. 2018Quantizing deep convolutional networks for efficient inference: A whitepapercited by 507
  5. 2019One Epoch Is All You Needcited by 41
  6. 2019ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retrainingcited by 28
  7. 2020TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPUcited by 17
  8. 2021Control Variate Approximation for DNN Acceleratorscited by 11
  9. 2016Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networkscited by 6
  10. 2018Mechanism to Mitigate AVX-Induced Frequency Reductioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 5; 2025: 3; 2026: 2.

11 papers.

  1. 2022Approximate Computing and the Efficient Machine Learning Expeditioncited by 8
  2. 2024TransAxx: Efficient Transformers with Approximate Computingcited by 6
  3. 2025AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Enginescited by 5
  4. 2024AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Acceleratorscited by 2
  5. 2024USEFUSE: Uniform Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networkscited by 1
  6. 2024Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multiplierscited by 0
  7. 2024FAMES: Fast Approximate Multiplier Substitution for Mixed-Precision Quantized DNNs--Down to 2 Bits!cited by 0
  8. 2025Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrayscited by 0
  9. 2025Gradient Estimation Methods of Approximate Multipliers for High-Accuracy Retraining of Deep Learning Modelscited by 0
  10. 2026AxMoE: Characterizing the Impact of Approximate Multipliers on Mixture-of-Experts DNN Architecturescited by 0
  11. 2026FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retrainingcited by 0