1
|
Aguirre F, Sebastian A, Le Gallo M, Song W, Wang T, Yang JJ, Lu W, Chang MF, Ielmini D, Yang Y, Mehonic A, Kenyon A, Villena MA, Roldán JB, Wu Y, Hsu HH, Raghavan N, Suñé J, Miranda E, Eltawil A, Setti G, Smagulova K, Salama KN, Krestinskaya O, Yan X, Ang KW, Jain S, Li S, Alharbi O, Pazos S, Lanza M. Hardware implementation of memristor-based artificial neural networks. Nat Commun 2024; 15:1974. [PMID: 38438350 PMCID: PMC10912231 DOI: 10.1038/s41467-024-45670-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 02/01/2024] [Indexed: 03/06/2024] Open
Abstract
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.
Collapse
Affiliation(s)
- Fernando Aguirre
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | | | | | - Wenhao Song
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - Tong Wang
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - Wei Lu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Meng-Fan Chang
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET, Piazza L. da Vinci 32, 20133, Milano, Italy
| | - Yuchao Yang
- School of Electronic and Computer Engineering, Peking University, Shenzhen, China
| | - Adnan Mehonic
- Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK
| | - Anthony Kenyon
- Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK
| | - Marco A Villena
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Juan B Roldán
- Departamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada, Avenida Fuentenueva s/n, 18071, Granada, Spain
| | - Yuting Wu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hung-Hsi Hsu
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Nagarajan Raghavan
- Engineering Product Development (EPD) Pillar, Singapore University of Technology & Design, 8 Somapah Road, 487372, Singapore, Singapore
| | - Jordi Suñé
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | - Enrique Miranda
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | - Ahmed Eltawil
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Gianluca Setti
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Kamilya Smagulova
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Khaled N Salama
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Olga Krestinskaya
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Samarth Jain
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Osamah Alharbi
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Sebastian Pazos
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Mario Lanza
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
| |
Collapse
|
3
|
Hsieh CC, Lu MR, Tseng HT. Automatic Speaker Positioning in Meetings Based on YOLO and TDOA. SENSORS (BASEL, SWITZERLAND) 2023; 23:6250. [PMID: 37514545 PMCID: PMC10384276 DOI: 10.3390/s23146250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/03/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023]
Abstract
In recent years, many things have been held via video conferences due to the impact of the COVID-19 epidemic around the world. A webcam will be used in conjunction with a computer and the Internet. However, the network camera cannot automatically turn and cannot lock the screen to the speaker. Therefore, this study uses the objection detector YOLO to capture the upper body of all people on the screen and judge whether each person opens or closes their mouth. At the same time, the Time Difference of Arrival (TDOA) is used to detect the angle of the sound source. Finally, the person's position obtained by YOLO is reversed to the person's position in the spatial coordinates through the distance between the person and the camera. Then, the spatial coordinates are used to calculate the angle between the person and the camera through inverse trigonometric functions. Finally, the angle obtained by the camera, and the angle of the sound source obtained by the microphone array, are matched for positioning. The experimental results show that the recall rate of positioning through YOLOX-Tiny reached 85.2%, and the recall rate of TDOA alone reached 88%. Integrating YOLOX-Tiny and TDOA for positioning, the recall rate reached 86.7%, the precision rate reached 100%, and the accuracy reached 94.5%. Therefore, the method proposed in this study can locate the speaker, and it has a better effect than using only one source.
Collapse
Affiliation(s)
- Chen-Chiung Hsieh
- Department of Computer Science and Engineering, Tatung University, Taipei City 104, Taiwan
| | - Men-Ru Lu
- Department of Computer Science and Engineering, Tatung University, Taipei City 104, Taiwan
| | - Hsiao-Ting Tseng
- Department of Information Management, National Central University, Taoyuan City 320, Taiwan
| |
Collapse
|