A low-power charge-based integrate-and-fire circuit for binarized-spiking neural network

Duong, Quang-Manh1; Trinh, Quang-Kien1; Nguyen, Van-Tinh1; Dao, Dinh-Ha1; Luong, Duy-Manh1; Hoang, Van-Phuc2; Lin, Longyang3; Deepu, John4

2023-02-01

10.1002/cta.3573

INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS   Impact Factor & Quartile

0098-9886

1097-007X

This paper presents a charge-based integrate-and-fire (IF) circuit for in-memory binary spiking neural networks (BSNNs). The proposed IF circuit can mimic both addition and subtraction operations that permit better incorporation with in-memory XNOR-based synapses to implement the BSNN processing core. To evaluate the proposed design, we have developed a framework that incorporates the circuit's imperfections effects into the system-level simulation. The array circuits use 2T-2J Spin-Transfer-Torque Magnetoresistive RAM (STT-MRAM) based on a 65-nm commercial CMOS and a fitted magnetic tunnel junction (MTJ). The system model has been described in Pytorch to best fit the extracted parameters from circuit levels, including the cover of device nonidealities and process variations. The simulation results show that the proposed design can achieve a performance of 5.10 fJ/synapse and reaches 82.01% classification accuracy for CIFAR-10 under process variation.

binary spiking neural networks in-memory computing neuromorphic computing STT-MRAM

SCI

English

Others

Vietnam National Foundation for Science and Technology Development (NAFOSTED)[102.01-2018.310] ; Microelectronic Circuit Centre Ireland[MCCI-2020-07]

Engineering

Engineering, Electrical & Electronic

WOS:000940208600001

WILEY

ENGINEERING

Web of Science

1.Le Quy Don Tech Univ, Fac Radioelect Engn, Hanoi, Vietnam
2.Le Quy Don Tech Univ, Inst Syst Integrat, Hanoi, Vietnam
3.Southern Univ Sci & Technol, Sch Microelect, Shenzhen, Peoples R China
4.Univ Coll Dublin, Sch Elect & Elect Engn, Dublin, Ireland

Duong, Quang-Manh,Trinh, Quang-Kien,Nguyen, Van-Tinh,et al. A low-power charge-based integrate-and-fire circuit for binarized-spiking neural network[J]. INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS,2023.

Duong, Quang-Manh.,Trinh, Quang-Kien.,Nguyen, Van-Tinh.,Dao, Dinh-Ha.,Luong, Duy-Manh.,...&Deepu, John.(2023).A low-power charge-based integrate-and-fire circuit for binarized-spiking neural network.INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS.

Duong, Quang-Manh,et al."A low-power charge-based integrate-and-fire circuit for binarized-spiking neural network".INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS (2023).