# Leveraging Highly Approximated Multipliers in DNN Inference

arXiv: https://arxiv.org/abs/2412.16757 · HTML: https://mlsanity.com/paper/2412.16757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 21 Dec 2024.

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

- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey](https://mlsanity.com/paper/2203.08737/) (2022)
- [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)
- [AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers](https://mlsanity.com/paper/1911.08097/) (2019)
- [Positive/Negative Approximate Multipliers for DNN Accelerators](https://mlsanity.com/paper/2107.09366/) (2021)
- [Reliability-Aware Quantization for Anti-Aging NPUs](https://mlsanity.com/paper/2103.04812/) (2021)

## Built on by (most cited first, 1 of 1)

- [Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainability](https://mlsanity.com/paper/2504.09851/) (2025)

## Abstract (arXiv)

In this work, we present a control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our approach does not require retraining and significantly decreases the induced error due to approximate multiplications, improving the overall inference accuracy. As a result, our approach enables satisfying tight accuracy loss constraints while boosting the power savings. Our experimental evaluation, across six different DNNs and several approximate multipliers, demonstrates the versatility of our approach and shows that compared to the accurate design, our control variate approximation achieves the same performance, 45% power reduction, and less than 1% average accuracy loss. Compared to the corresponding approximate designs without using our technique, our approach improves the accuracy by 1.9x on average.