Fast Generation of Custom Floating-Point Spatial Filters on FPGAs
Convolutional Neural Networks (CNNs) have been utilised in many image and video processing applications. The convolution operator, also known as a spatial filter, is usually a linear operation, but this linearity compromises essential features and details inherent in the non-linearity present in man...
Saved in:
Main Authors: | Nelson Campos, Eran Edirisinghe, Slava Chesnokov, Daniel Larkin |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10734090/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
FPGA‐based implementation of floating point processing element for the design of efficient FIR filters
by: Tintu Mary John, et al.
Published: (2021-07-01) -
Multi‐precision binary multiplier architecture for multi‐precision floating‐point multiplication
by: Geetam Singh Tomar, et al.
Published: (2021-08-01) -
Modified fast discrete‐time PID formulas for obtaining double precision accuracy
by: Eungnam Kim, et al.
Published: (2024-12-01) -
FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
by: Mustafa Tasci, et al.
Published: (2025-01-01) -
New High-Speed Arithmetic Circuits Based on Spiking Neural P Systems with Communication on Request Implemented in a Low-Area FPGA
by: José Rangel, et al.
Published: (2024-11-01)