ReRAM In-Memory Computing for Online Reinforcement Learning

ReRAM In-Memory Computing for Online Reinforcement Learning

Presenter(s)

Md Shahanur Alam

Comments

Presentation: 9:00 a.m.-9:20 a.m., Kennedy Union 311

Description

Reinforcement learning (RL) has been examined to learn when an agent interacts continually with an environment to learn an optimal policy. Neuromorphic in-memory computing is a computing method that can be used to implement Artificial Intelligence (AI) on low power. Complementary-Metal-Oxide-Semiconductor (SRAM or DRAM) based in-memory computing systems have been developed for AI inference applications at the edge. These models are not able to perform on-chip training. Alternatively, significant progress has been made in Non-Volatile Memory (NVM) based systems that allow for on-chip training. The Resistive-RAM or ReRAM is an emerging NVM device, which has been examined for implementing in-memory computing systems in the analog domain. However, ReRAM neuromorphic systems have not been investigated extensively for the RL algorithm. This work presents a memristor crossbar circuit for on-chip reinforcement learning, where the learning process takes place in a dynamic environment. The success of learning is ensured by achieving the optimum average score of the agent in the presence of environmental variability.

Publication Date

4-20-2022

Project Designation

Graduate Research

Primary Advisor

Tarek M. Taha

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium project, School of Engineering

ReRAM In-Memory Computing for Online Reinforcement Learning

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