[2PS-007]
우수논문발표상 응모자Physical Implementation of Reinforcement Learning with a Dual-Input Synaptic Transistor
발표자유지현 (연세대학교)
연구책임자조정호 (연세대학교)
Abstract
The growing computational cost of modern artificial intelligence (AI) workloads motivates hardware approaches that reduce energy consumption and circuit overhead. We report a dual-input synaptic transistor (DIST) that realizes in-device analog current summation by combining independent voltage and light driven conductance updates. A photoactive Au(I) complex (Au(DippPZI)(DPA)) undergoes dipole inversion under illumination, while -OH trap sites in crosslinked PVP enable gate voltage modulation of an IGZO channel. Optical and electrical pulses add linearly with little interference, enabling direct mapping to value-advantage integration in a dueling deep Q-network. In a Python racing task, a DIST-based agent learns stably and matches software-level performance, highlighting a scalable route toward device-level acceleration of reinforcement learning inference and training.