Highly parallel and ultra-low-power probabilistic reasoning with programmable gaussian-like memory transistors - Changhyeon Lee; Leila Rahimifard; Junhwan Choi; Jeong-ik Park; Chungryeol Lee; Divake Kumar; Priyesh Shukla; Seung Min Lee; Amit Ranjan Trivedi; Hocheon Yoo; Sung Gap Im (Nature Communications, 2024)
Abstract Probabilistic inference in data-driven models is promising for predicting outputs and associated confidence levels, alleviating risks arising from overconfidence. However, implementing complex computations with minimal devices still remains challenging. Here, utilizing a heterojunction of p- and n-type semiconductors coupled with separate floating-gate configuration, a Gaussian-like memory transistor is proposed, where a programmable Gaussian-like current-voltage response is achieved within a single device. A separate floating-gate structure allows for exquisite control of the Gaussian-like current output to a significant extent through simple programming, with an over 10000 s retention performance and mechanical flexibility. This enables physical evaluation of complex distribution functions with the simplified circuit design and higher parallelism. Successful implementation for localization and obstacle avoidance tasks is demonstrated using Gaussian-like curves produced from Gaussian-like memory transistor. With its ultralow-power consumption, simplified design, and programmable Gaussian-like outputs, our 3-terminal Gaussian-like memory transistor holds potential as a hardware platform for probabilistic inference computing.