# Approximate Computing and the Efficient Machine Learning Expedition

arXiv: https://arxiv.org/abs/2210.00497 · HTML: https://mlsanity.com/paper/2210.00497/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 81 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Learning Structured Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1608.03665/) (2016)
- [PACT: Parameterized Clipping Activation for Quantized Neural Networks](https://mlsanity.com/paper/1805.06085/) (2018)
- [Q8BERT: Quantized 8Bit BERT](https://mlsanity.com/paper/1910.06188/) (2019)
- [EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://mlsanity.com/paper/1602.01528/) (2016)
- [Improving Neural Network Quantization without Retraining using Outlier Channel Splitting](https://mlsanity.com/paper/1901.09504/) (2019)
- [Staleness-aware Async-SGD for Distributed Deep Learning](https://mlsanity.com/paper/1511.05950/) (2015)
- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [Fully Quantized Transformer for Machine Translation](https://mlsanity.com/paper/1910.10485/) (2019)
- [AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training](https://mlsanity.com/paper/1712.02679/) (2017)
- [NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference](https://mlsanity.com/paper/2112.02191/) (2021)
- [Defensive Approximation: Securing CNNs using Approximate Computing](https://mlsanity.com/paper/2006.07700/) (2020)
- [Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise](https://mlsanity.com/paper/2109.05437/) (2021)
- [Cross-Layer Approximation For Printed Machine Learning Circuits](https://mlsanity.com/paper/2203.05915/) (2022)
- [Control Variate Approximation for DNN Accelerators](https://mlsanity.com/paper/2102.09642/) (2021)
- [AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch](https://mlsanity.com/paper/2203.04071/) (2022)
- [Discretization based Solutions for Secure Machine Learning against Adversarial Attacks](https://mlsanity.com/paper/1902.03151/) (2019)
- [SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks](https://mlsanity.com/paper/1711.06315/) (2017)
- [Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits](https://mlsanity.com/paper/2203.08011/) (2022)
- [Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing](https://mlsanity.com/paper/1602.08557/) (2016)

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

- [Reservoir Computing Using Measurement-Controlled Quantum Dynamics](https://mlsanity.com/paper/2403.01024/) (2024)
- [Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons](https://mlsanity.com/paper/2312.17612/) (2023)
- [Embedding Hardware Approximations in Discrete Genetic-based Training for Printed MLPs](https://mlsanity.com/paper/2402.02930/) (2024)
- [On-sensor Printed Machine Learning Classification via Bespoke ADC and Decision Tree Co-Design](https://mlsanity.com/paper/2312.01172/) (2023)
- [Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables](https://mlsanity.com/paper/2508.19661/) (2025)
- [Compact Yet Highly Accurate Printed Classifiers Using Sequential Support Vector Machine Circuits](https://mlsanity.com/paper/2502.01498/) (2025)
- [Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation](https://mlsanity.com/paper/2508.19660/) (2025)
- [Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables](https://mlsanity.com/paper/2511.05985/) (2025)

## Abstract (arXiv)

Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC research activities in various application domains, AxC thrived the past decade when it was applied in Machine Learning (ML). The by definition approximate notion of ML models but also the increased computational overheads associated with ML applications-that were effectively mitigated by corresponding approximations-led to a perfect matching and a fruitful synergy. AxC for AI/ML has transcended beyond academic prototypes. In this work, we enlighten the synergistic nature of AxC and ML and elucidate the impact of AxC in designing efficient ML systems. To that end, we present an overview and taxonomy of AxC for ML and use two descriptive application scenarios to demonstrate how AxC boosts the efficiency of ML systems.