In situunsupervised learning using stochastic switching in magneto-electric magnetic tunnel junctions
Abstract
Spiking neural networks (SNNs) offer a bio-plausible and potentially power-efficient alternative to conventional deep learning. Although there has been progress towards implementing SNN functionalities in custom CMOS-based hardware using beyond Von Neumann architectures, the power-efficiency of the human brain has remained elusive. This has necessitated investigations of novel material systems which can efficiently mimic the functional units of SNNs, such as neurons and synapses. In this paper, we present a magnetoelectric–magnetic tunnel junction (ME-MTJ) device as a synapse. We arrange these synapses in a crossbar fashion and performin situunsupervised learning. We leverage the capacitive nature of write-ports in ME-MTJs, wherein by applying appropriately shaped voltage pulses across the write-port, the ME-MTJ can be switched in a probabilistic manner. We further exploit the sigmoidal switching characteristics of ME-MTJ to tune the synapses to follow the well-known spike timing-dependent plasticity (STDP) rule in a stochastic fashion. Finally, we use the stochastic STDP rule in ME-MTJ synapses to simulate a two-layered SNN to perform image classification tasks on a handwritten digit dataset. Thus, the capacitive write-port and the decoupled-nature of read-write path of ME-MTJs allow us to construct a transistor-less crossbar, suitable for energy-efficient implementation ofin situlearning in SNNs.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 23, 2019
- Source ID
- 10.1098/rsta.2019.0157
Entities
People
- Akhilesh Jaiswal
- Amogh Agrawal
- Gopalakrishnan Srinivasan
- Indranil Chakraborty
- Kaushik Roy
Organizations
- Defense Advanced Research Projects Agency
- Intel Corporation
- National Science Foundation
- Purdue University
- Semiconductor Research Corporation