CMOS Implementation of Spiking Equilibrium Propagation for Real-Time Learning

2022 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS 2022): INTELLIGENT TECHNOLOGY IN THE POST-PANDEMIC ERA(2022)

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Abstract
Equilibrium propagation (EqProp) and its adaptations for spiking neural networks (SNN) are presented as biologically plausible alternatives to back-propagation (BP) which describe a potential low-energy means of learning complex tasks in neuromorphic hardware. These algorithms are conducive to extremely efficient analog computing approaches, but a detailed analog circuit implementation and architectural outline have not yet been presented. Furthermore, current theoretical analog designs of EqProp have not addressed synapse circuit-level implementations capable of simultaneous sensing and weight updates for real-time learning. To this end, we have designed and simulated a circuit-level implementation of a spiking EqProp neuron and synapse in CMOS 65 nm technology capable of concurrent inference and weight updates for real-time learning.
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Key words
neuromorphic,CMOS,equilibrium propagation
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