Analogue fast Fourier transforms for scalable and efficient signal processing
- 19 October 2026
- journal article
- research article
- Published by Springer Nature in Nature Electronics
- p. 1-14
Abstract
No abstract availableKeywords
Funding Information
- U.S. Department of Energy (DE-NA0003525)
- U.S. Department of Energy (DE-NA0003525)
- U.S. Department of Energy (DE-NA0003525)
- U.S. Department of Energy (DE-NA0003525)
- U.S. Department of Energy (DE-NA0003525)
- U.S. Department of Energy (DE-NA0003525)
- U.S. Department of Energy (DE-NA0003525)
- U.S. Department of Energy (DE-NA0003525)
- U.S. Department of Energy (DE-NA0003525)
- U.S. Department of Energy (DE-NA0003525)
This publication has 49 references indexed in Scilit:
- Hardware implementation of memristor-based artificial neural networksNature Communications, 2024
- Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based acceleratorsNature Communications, 2023
- An analog-AI chip for energy-efficient speech recognition and transcriptionNature, 2023
- Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosisNature Communications, 2023
- A compute-in-memory chip based on resistive random-access memoryNature, 2022
- Memory devices and applications for in-memory computingNature Nanotechnology, 2020
- Fully hardware-implemented memristor convolutional neural networkNature, 2020
- Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product EngineAdvanced Materials, 2018
- Analogue signal and image processing with large memristor crossbarsNature Electronics, 2017
- Fast fourier transforms: A tutorial review and a state of the artSignal Processing, 1990