# Neural Network-Hardware Co-design for Scalable RRAM-based BNN Accelerators

arXiv: https://arxiv.org/abs/1811.02187 · HTML: https://mlsanity.com/paper/1811.02187/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [StoX-Net: Stochastic Processing of Partial Sums for Efficient In-Memory Computing DNN Accelerators](https://mlsanity.com/paper/2407.12378/) (2024)
- [Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer](https://mlsanity.com/paper/2505.14303/) (2025)
- [PR-CIM: a Variation-Aware Binary-Neural-Network Framework for Process-Resilient Computation-in-memory](https://mlsanity.com/paper/2110.09962/) (2021)

## Abstract (arXiv)

Recently, RRAM-based Binary Neural Network (BNN) hardware has been gaining interests as it requires 1-bit sense-amp only and eliminates the need for high-resolution ADC and DAC. However, RRAM-based BNN hardware still requires high-resolution ADC for partial sum calculation to implement large-scale neural network using multiple memory arrays. We propose a neural network-hardware co-design approach to split input to fit each split network on a RRAM array so that the reconstructed BNNs calculate 1-bit output neuron in each array. As a result, ADC can be completely eliminated from the design even for large-scale neural network. Simulation results show that the proposed network reconstruction and retraining recovers the inference accuracy of the original BNN. The accuracy loss of the proposed scheme in the CIFAR-10 testcase was less than 1.1% compared to the original network. The code for training and running proposed BNN models is available at: https://github.com/YulhwaKim/RRAMScalable_BNN.