An Energy Efficient EdgeAI Autoencoder Accelerator for Reinforcement Learning

An Energy Efficient EdgeAI Autoencoder Accelerator for Reinforcement Learning

In EdgeAI embedded devices that exploit reinforcement learning (RL), it is essential to reduce the number of actions taken by the agent in the real world and minimize the compute-intensive policies learning process. Convolutional autoencoders (AEs) has demonstrated great improvement for speeding up...

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Bibliographic Details
Main Authors: Nitheesh Kumar Manjunath, Aidin Shiri, Morteza Hosseini, Bharat Prakash, Nicholas R. Waytowich, Tinoosh Mohsenin
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Open Journal of Circuits and Systems
Subjects:
Reinforcement learning
autonomous systems
autoencoder
binary neural networks (BNNs)
ternary neural networks (TNNs)
EdgeAI
Online Access:https://ieeexplore.ieee.org/document/9335309/

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