# Comprehensive Benchmarking of Binary Neural Networks on NVM Crossbar Architectures

arXiv: https://arxiv.org/abs/2308.06227 · HTML: https://mlsanity.com/paper/2308.06227/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Binary Neural Networks: A Survey](https://mlsanity.com/paper/2004.03333/) (2020)
- [ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions](https://mlsanity.com/paper/2003.03488/) (2020)
- [Towards Accurate Binary Convolutional Neural Network](https://mlsanity.com/paper/1711.11294/) (2017)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference](https://mlsanity.com/paper/1901.10351/) (2019)
- [PokeBNN: A Binary Pursuit of Lightweight Accuracy](https://mlsanity.com/paper/2112.00133/) (2021)
- [A Modern Primer on Processing in Memory](https://mlsanity.com/paper/2012.03112/) (2020)
- [FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations](https://mlsanity.com/paper/2012.12206/) (2020)
- [RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars](https://mlsanity.com/paper/1809.00072/) (2018)
- [PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAM](https://mlsanity.com/paper/1912.11516/) (2019)
- [Join the High Accuracy Club on ImageNet with A Binary Neural Network Ticket](https://mlsanity.com/paper/2211.12933/) (2022)
- [Binarized Neural Machine Translation](https://mlsanity.com/paper/2302.04907/) (2023)
- [TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems](https://mlsanity.com/paper/2002.11151/) (2020)
- [BCNN: Binary Complex Neural Network](https://mlsanity.com/paper/2104.10044/) (2021)

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

- [Attack on a PUF-based Secure Binary Neural Network](https://mlsanity.com/paper/2510.24422/) (2025)

## Abstract (arXiv)

Non-volatile memory (NVM) crossbars have been identified as a promising technology, for accelerating important machine learning operations, with matrix-vector multiplication being a key example. Binary neural networks (BNNs) are especially well-suited for use with NVM crossbars due to their use of a low-bitwidth representation for both activations and weights. However, the aggressive quantization of BNNs can result in suboptimal accuracy, and the analog effects of NVM crossbars can further degrade the accuracy during inference. This paper presents a comprehensive study that benchmarks BNNs trained and validated on ImageNet and deployed on NeuroSim, a simulator for NVM-crossbar-based PIM architecture. Our study analyzes the impact of various parameters, such as input precision and ADC resolution, on both the accuracy of the inference and the hardware performance metrics. We have found that an ADC resolution of 8-bit with an input precision of 4-bit achieves near-optimal accuracy compared to the original BNNs. In addition, we have identified bottleneck components in the PIM architecture that affect area, latency, and energy consumption, and we demonstrate the impact that different BNN layers have on hardware performance.