| Title | A Biomimetic Tunnel FET-Based Spiking Neuron for Energy-Efficient Neuromorphic Computing With Reduced Hardware Cost |
| Authors | Luo, Jin Chen, Cheng Huang, Qianqian Huang, Ru |
| Affiliation | Peking Univ, Sch Integrated Circuits, Key Lab Microelect Devices & Circuits MOE, Beijing 100871, Peoples R China Peking Univ, Beijing Lab Future IC Technol & Sci, Beijing 100871, Peoples R China Chinese Inst Brain Res CIBR, Beijing 102206, Peoples R China |
| Issue Date | Dec-2021 |
| Publisher | IEEE TRANSACTIONS ON ELECTRON DEVICES |
| Abstract | In this work, utilizing the unique features of conventional Si-based tunnel FET (TFET), a TFET-based leaky integrate-and-fire (LIF) neuron with higher energy efficiency and reduced hardware cost is proposed. Compared with traditional CMOS-based LIF neuron, the proposed TFET-based LIF neuron can produce an additional bio-plausible after-hyperpolarization (AHP) behavior and relative refractory period without extra hardware cost by exploiting the features of large Miller effect and forward p-i-n current in TFET. Moreover, the typical ambipolar effect and superlinear onset behaviors in conventional Si-based TFET enable the lower hardware cost and lower energy consumption (similar to 10x reduction) for TFET-based neuron. Furthermore, the proposed TFET neuron-based spiking neural network (SNN) is demonstrated for pattern recognition tasks, showing its advantage of significant energy efficiency. This work provides a promising highly integrated and energy-efficient solution for the hardware implementation of spiking neuron for neuromorphic computing. |
| URI | http://hdl.handle.net/20.500.11897/632847 |
| ISSN | 0018-9383 |
| DOI | 10.1109/TED.2021.3131633 |
| Indexed | EI SCI(E) |
| Appears in Collections: | 待认领 |
