# daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices

arXiv: https://arxiv.org/abs/1908.05858 · HTML: https://mlsanity.com/paper/1908.05858/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 27 of 27)

- [HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark](https://mlsanity.com/paper/2103.10584/) (2021)
- [How Do Adam and Training Strategies Help BNNs Optimization?](https://mlsanity.com/paper/2106.11309/) (2021)
- [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (2021)
- [DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection](https://mlsanity.com/paper/2101.06896/) (2021)
- [Distribution Adaptive INT8 Quantization for Training CNNs](https://mlsanity.com/paper/2102.04782/) (2021)
- [Distribution-sensitive Information Retention for Accurate Binary Neural Network](https://mlsanity.com/paper/2109.12338/) (2021)
- [Binarized Spectral Compressive Imaging](https://mlsanity.com/paper/2305.10299/) (2023)
- [BiBench: Benchmarking and Analyzing Network Binarization](https://mlsanity.com/paper/2301.11233/) (2023)
- [Binarized Diffusion Model for Image Super-Resolution](https://mlsanity.com/paper/2406.05723/) (2024)
- [MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation](https://mlsanity.com/paper/2507.04290/) (2025)
- [BiDM: Pushing the Limit of Quantization for Diffusion Models](https://mlsanity.com/paper/2412.05926/) (2024)
- [Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors](https://mlsanity.com/paper/2205.12781/) (2022)
- [BiFSMN: Binary Neural Network for Keyword Spotting](https://mlsanity.com/paper/2202.06483/) (2022)
- [Fast matrix multiplication for binary and ternary CNNs on ARM CPU](https://mlsanity.com/paper/2205.09120/) (2022)
- [Lightweight and Progressively-Scalable Networks for Semantic Segmentation](https://mlsanity.com/paper/2207.13600/) (2022)
- [BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance](https://mlsanity.com/paper/2211.06987/) (2022)
- [Optimizing data-flow in Binary Neural Networks](https://mlsanity.com/paper/2304.00952/) (2023)
- [Binary Neural Networks as a general-propose compute paradigm for on-device computer vision](https://mlsanity.com/paper/2202.03716/) (2022)
- [RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of Quantized CNNs](https://mlsanity.com/paper/2301.06193/) (2023)
- [YFlows: Systematic Dataflow Exploration and Code Generation for Efficient Neural Network Inference using SIMD Architectures on CPUs](https://mlsanity.com/paper/2310.00574/) (2023)
- [BinaryHPE: 3D Human Pose and Shape Estimation via Binarization](https://mlsanity.com/paper/2311.14323/) (2023)
- [LAB: Learnable Activation Binarizer for Binary Neural Networks](https://mlsanity.com/paper/2210.13858/) (2022)
- [Exploiting Kernel Compression on BNNs](https://mlsanity.com/paper/2212.00608/) (2022)
- [Efficient Feature Description for Small Body Relative Navigation using Binary Convolutional Neural Networks](https://mlsanity.com/paper/2304.04985/) (2023)
- [Neural Network Compression using Binarization and Few Full-Precision Weights](https://mlsanity.com/paper/2306.08960/) (2023)
- [Efficiera Residual Networks: Hardware-Friendly Fully Binary Weight with 2-bit Activation Model Achieves Practical ImageNet Accuracy](https://mlsanity.com/paper/2410.11553/) (2024)
- [EeveeDark: A Binary Neural Framework for Low-Light Video Enhancement via Event-Guided Sensor-Level Fusion](https://mlsanity.com/paper/2607.06217/) (2026)

## Abstract (arXiv)

It is always well believed that Binary Neural Networks (BNNs) could drastically accelerate the inference efficiency by replacing the arithmetic operations in float-valued Deep Neural Networks (DNNs) with bit-wise operations. Nevertheless, there has not been open-source implementation in support of this idea on low-end ARM devices (e.g., mobile phones and embedded devices). In this work, we propose daBNN --- a super fast inference framework that implements BNNs on ARM devices. Several speed-up and memory refinement strategies for bit-packing, binarized convolution, and memory layout are uniquely devised to enhance inference efficiency. Compared to the recent open-source BNN inference framework, BMXNet, our daBNN is 7×~23× faster on a single binary convolution, and about 6× faster on Bi-Real Net 18 (a BNN variant of ResNet-18). The daBNN is a BSD-licensed inference framework, and its source code, sample projects and pre-trained models are available on-line: https://github.com/JDAI-CV/dabnn.