# Control Variate Approximation for DNN Accelerators

arXiv: https://arxiv.org/abs/2102.09642 · HTML: https://mlsanity.com/paper/2102.09642/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining](https://mlsanity.com/paper/1907.07229/) (2019)
- [SCALE-Sim: Systolic CNN Accelerator Simulator](https://mlsanity.com/paper/1811.02883/) (2018)
- [TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU](https://mlsanity.com/paper/2002.09481/) (2020)

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

- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch](https://mlsanity.com/paper/2203.04071/) (2022)
- [Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits](https://mlsanity.com/paper/2302.14576/) (2023)
- [Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization](https://mlsanity.com/paper/2312.15322/) (2023)
- [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)
- [Positive/Negative Approximate Multipliers for DNN Accelerators](https://mlsanity.com/paper/2107.09366/) (2021)
- [Leveraging Highly Approximated Multipliers in DNN Inference](https://mlsanity.com/paper/2412.16757/) (2024)
- [Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration](https://mlsanity.com/paper/2207.12350/) (2022)
- [Low Error-Rate Approximate Multiplier Design for DNNs with Hardware-Driven Co-Optimization](https://mlsanity.com/paper/2210.03916/) (2022)
- [Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays](https://mlsanity.com/paper/2503.16583/) (2025)

## Abstract (arXiv)

In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate technique is used in Monte Carlo methods to achieve variance reduction. Our approach significantly decreases the induced error due to approximate multiplications in DNN inference, without requiring time-exhaustive retraining compared to state-of-the-art. Leveraging our control variate method, we use highly approximated multipliers to generate power-optimized DNN accelerators. Our experimental evaluation on six DNNs, for Cifar-10 and Cifar-100 datasets, demonstrates that, compared to the accurate design, our control variate approximation achieves same performance and 24% power reduction for a merely 0.16% accuracy loss.