# Energy Efficient Hadamard Neural Networks

arXiv: https://arxiv.org/abs/1805.05421 · HTML: https://mlsanity.com/paper/1805.05421/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2018.

## Built on by (most cited first, 12 of 12)

- [Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks](https://mlsanity.com/paper/2104.07085/) (2021)
- [Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks](https://mlsanity.com/paper/2201.02711/) (2022)
- [A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer](https://mlsanity.com/paper/2305.17510/) (2023)
- [A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks](https://mlsanity.com/paper/2104.05124/) (2021)
- [Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets](https://mlsanity.com/paper/2303.06797/) (2023)
- [Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Dataset](https://mlsanity.com/paper/2502.10452/) (2025)
- [HTMA-Net: Towards Multiplication-Avoiding Neural Networks via Hadamard Transform and In-Memory Computing](https://mlsanity.com/paper/2509.23103/) (2025)
- [Source Feature Compression for Object Classification in Vision-Based Underwater Robotics](https://mlsanity.com/paper/2112.13953/) (2021)
- [Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method](https://mlsanity.com/paper/2209.09106/) (2022)
- [Multipod Convolutional Network](https://mlsanity.com/paper/2210.00689/) (2022)
- [WTHaar-Net: a Hybrid Quantum-Classical Approach](https://mlsanity.com/paper/2603.02497/) (2026)
- [WHTMix: Efficient Stereo Depth Estimation via Walsh-Hadamard Token Mixing](https://mlsanity.com/paper/2607.25234/) (2026)

## Abstract (arXiv)

Deep learning has made significant improvements at many image processing tasks in recent years, such as image classification, object recognition and object detection. Convolutional neural networks (CNN), which is a popular deep learning architecture designed to process data in multiple array form, show great success to almost all detection & recognition problems and computer vision tasks. However, the number of parameters in a CNN is too high such that the computers require more energy and larger memory size. In order to solve this problem, we propose a novel energy efficient model Binary Weight and Hadamard-transformed Image Network (BWHIN), which is a combination of Binary Weight Network (BWN) and Hadamard-transformed Image Network (HIN). It is observed that energy efficiency is achieved with a slight sacrifice at classification accuracy. Among all energy efficient networks, our novel ensemble model outperforms other energy efficient models.