<italic>ExPAN(N)D</italic>: Exploring Posits for Efficient Artificial Neural Network Design in FPGA-Based Systems

<italic>ExPAN(N)D</italic>: Exploring Posits for Efficient Artificial Neural Network Design in FPGA-Based Systems

The high computational complexity, memory footprints, and energy requirements of machine learning models, such as Artificial Neural Networks (ANNs), hinder their deployment on resource-constrained embedded systems. Most state-of-the-art works have considered this problem by proposing various low bit...

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Bibliographic Details
Main Authors: Suresh Nambi, Salim Ullah, Siva Satyendra Sahoo, Aditya Lohana, Farhad Merchant, Akash Kumar
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Computer arithmetic
deep neural networks
energy efficient computing
posits
FPGA
high-level synthesis
Online Access:https://ieeexplore.ieee.org/document/9492075/

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