# Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approach

arXiv: https://arxiv.org/abs/2505.24721 · HTML: https://mlsanity.com/paper/2505.24721/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings](https://mlsanity.com/paper/2304.01433/) (2023)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays](https://mlsanity.com/paper/2104.02184/) (2021)
- [Flashlight: Enabling Innovation in Tools for Machine Learning](https://mlsanity.com/paper/2201.12465/) (2022)
- [RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars](https://mlsanity.com/paper/1809.00072/) (2018)
- [RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks](https://mlsanity.com/paper/1608.00895/) (2016)
- [Scaling Limits of Memristor-Based Routers for Asynchronous Neuromorphic Systems](https://mlsanity.com/paper/2307.08116/) (2023)
- [Synaptogen: A cross-domain generative device model for large-scale neuromorphic circuit design](https://mlsanity.com/paper/2404.06344/) (2024)
- [Neuromorphic Circuit Simulation with Memristors: Design and Evaluation Using MemTorch for MNIST and CIFAR](https://mlsanity.com/paper/2407.13410/) (2024)

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

- [Positional Encoding in the Context of Memristor-Based Analog Computation for Automatic Speech Recognition](https://mlsanity.com/paper/2606.13379/) (2026)

## Abstract (arXiv)

Memristor-based hardware offers new possibilities for energy-efficient machine learning (ML) by providing analog in-memory matrix multiplication. Current hardware prototypes cannot fit large neural networks, and related literature covers only small ML models for tasks like MNIST or single word recognition. Simulation can be used to explore how hardware properties affect larger models, but existing software assumes simplified hardware. We propose a PyTorch-based library based on "Synaptogen" to simulate neural network execution with accurately captured memristor hardware properties. For the first time, we show how an ML system with millions of parameters would behave on memristor hardware, using a Conformer trained on the speech recognition task TED-LIUMv2 as example. With adjusted quantization-aware training, we limit the relative degradation in word error rate to 25% when using a 3-bit weight precision to execute linear operations via simulated analog computation.