FPGA-Based Optical Surface Inspection of Wind Turbine Rotor Blades Using Quantized Neural Networks
Quantization of the weights and activations of a neural network is a way to drastically reduce necessary memory accesses and to replace arithmetic operations with bit-wise operations. This is especially beneficial for the implementation on field-programmable gate array (FPGA) technology that is part...
Main Authors: | Lino Antoni Giefer, Benjamin Staar, Michael Freitag |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-11-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/9/11/1824 |
Similar Items
-
Reconfigurable Antenna Array Testbed for Quantized Controlling
by: Michal Pokorny, et al.
Published: (2024-01-01) -
Super-Resolution Model Quantized in Multi-Precision
by: Jingyu Liu, et al.
Published: (2021-09-01) -
Two Novel Non-Uniform Quantizers with Application in Post-Training Quantization
by: Zoran Perić, et al.
Published: (2022-09-01) -
Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI
by: Mara Pistellato, et al.
Published: (2023-05-01) -
The Quantization of Gravity: Quantization of the Hamilton Equations
by: Claus Gerhardt
Published: (2021-04-01)