# ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing

arXiv: https://arxiv.org/abs/2504.17929 · HTML: https://mlsanity.com/paper/2504.17929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles](https://mlsanity.com/paper/2010.13972/) (2020)
- [Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA](https://mlsanity.com/paper/2203.05095/) (2022)
- [Turning Internet of Things(IoT) into Internet of Vulnerabilities (IoV) : IoT Botnets](https://mlsanity.com/paper/1702.03681/) (2017)
- [Gradient Backpropagation based Feature Attribution to Enable Explainable-AI on the Edge](https://mlsanity.com/paper/2210.10922/) (2022)
- [Non-Uniform Interpolation in Integrated Gradients for Low-Latency Explainable-AI](https://mlsanity.com/paper/2302.11107/) (2023)
- [Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units](https://mlsanity.com/paper/2103.11927/) (2021)
- [Exposing Reliability Degradation and Mitigation in Approximate DNNs under Permanent Faults](https://mlsanity.com/paper/2301.07484/) (2023)
- [Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?](https://mlsanity.com/paper/2112.01555/) (2021)

## Abstract (arXiv)

Explainable artificial intelligence (XAI) enhances AI system transparency by framing interpretability as an optimization problem. However, this approach often necessitates numerous iterations of computationally intensive operations, limiting its applicability in real-time scenarios. While recent research has focused on XAI hardware acceleration on FPGAs and TPU, these methods do not fully address energy efficiency in real-time settings. To address this limitation, we propose XAIedge, a novel framework that leverages approximate computing techniques into XAI algorithms, including integrated gradients, model distillation, and Shapley analysis. XAIedge translates these algorithms into approximate matrix computations and exploits the synergy between convolution, Fourier transform, and approximate computing paradigms. This approach enables efficient hardware acceleration on TPU-based edge devices, facilitating faster real-time outcome interpretations. Our comprehensive evaluation demonstrates that XAIedge achieves a 2× improvement in energy efficiency compared to existing accurate XAI hardware acceleration techniques while maintaining comparable accuracy. These results highlight the potential of XAIedge to significantly advance the deployment of explainable AI in energy-constrained real-time applications.