Resource and Energy Efficient Implementation of ECG Classifier using Binarized CNN for Edge AI Devices

Source : Verilog HDL

Cost : Rs. 55,000/- ( Verilog HDL + MATLAB GUI Code)

Base Paper Abstract:

Wearable Artificial Intelligence-of-Things (AIoT) devices demand smart gadgets that are both resource and energy-efficient. In this paper, we explore efficient implementation of binary convolutional neural network employing function merging and block reuse techniques. The hardware implemented in field programmable gate array (FPGA) platform can classify ventricular beat in electrocardiogram achieving accuracy of 97.5%, sensitivity of 85.7%, specificity of 99.0%, precision of 92.3%, and F1-score of 88.9% while consuming only 10.5-µW of dynamic power dissipation.

List of the following materials will be included with the Downloaded Backup:

1. Source code ( Modelsim/ Xilinx/ Quartus/ DSCH3/ Microwind)

2. Existing and Proposed Project Comparison with output video

3. Basic Documentation (20 to 30 Pages):

3.1 Proposed Title

3.2 Proposed Abstract

3.3 Advantages & Disadvantages

3.4 Improvement of this Project

3.5 Existing System with Notes

3.6 Proposed System with Notes

3.7 Literature Survey

3.8 Software Related Notes

3.9 VLSI and HDL Language / Tanner Notes

3.10 References & Reference Paper for More Pages

4. Online Support ( Any Desk / Zoom / Google Meet)

Objective of this project:

To develop a binarized convolutional neural network (bCNN) model for classifying normal and abnormal ECG beats with high efficiency.
To preprocess ECG signals using Sobel edge detection and convert them into binary image representations suitable for CNN input.
To reduce image dimensionality through multiple stages of max-pooling, down to an 8×8 resolution for efficient processing.
To implement the classification using a Dense1 and Dense2 architecture with only 16 neurons, significantly reducing complexity compared to the existing 256-neuron models.
To implement the complete classification pipeline on a Zynq FPGA using Verilog HDL, ensuring low power and minimal resource utilization

Software Implementation:

Modelsim
Xilinx

” Thanks for Visit this project Pages – Buy It Soon “

FPGA-Accelerated Binarized CNN for Abnormal ECG Detection Using Edge-Based Image Processing Techniques

Terms & Conditions:

Customer are advice to watch the project video file output, before the payment to test the requirement, correction will be applicable.
After payment, if any correction in the Project is accepted, but requirement changes is applicable with updated charges based upon the requirement.
After payment the student having doubts, correction, software error, hardware errors, coding doubts are accepted.
Online support will not be given more than 3 times.
On first time explanations we can provide completely with video file support, other 2 we can provide doubt clarifications only.
If any Issue on Software license / System Error we can support and rectify that within end of the day.
Extra Charges For duplicate bill copy. Bill must be paid in full, No part payment will be accepted.
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Resource and Energy Efficient Implementation of ECG Classifier using Binarized CNN for Edge AI Devices

Software Implementation:

FPGA-Accelerated Binarized CNN for Abnormal ECG Detection Using Edge-Based Image Processing Techniques

Terms & Conditions:

Bank Accounts

BUY VLSI PROJECTS ON ON-LINE”
“REFER YOUR FRIENDS WHO WILL NEED VLSI PROJECTS”
“EARN Rs.1,000/- FOR ONE VLSI PROJECT”

(Amount will given After Registration)

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2014

2015

2016

2017

2018

2019

Resource and Energy Efficient Implementation of ECG Classifier using Binarized CNN for Edge AI Devices

Software Implementation:

FPGA-Accelerated Binarized CNN for Abnormal ECG Detection Using Edge-Based Image Processing Techniques

Terms & Conditions:

Bank Accounts

BUY VLSI PROJECTS ON ON-LINE”“REFER YOUR FRIENDS WHO WILL NEED VLSI PROJECTS”“EARN Rs.1,000/- FOR ONE VLSI PROJECT”

(Amount will given After Registration)

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THANK YOU

Our services

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Contact us :

Our services

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Contact us :

BUY VLSI PROJECTS ON ON-LINE”
“REFER YOUR FRIENDS WHO WILL NEED VLSI PROJECTS”
“EARN Rs.1,000/- FOR ONE VLSI PROJECT”