Blog for ML_model_on_FPGA

_posts/2023-08-24-ML-model-on-FPGA.md

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+---
+layout: post
+title : ML Model on FPGA
+tags : FPGA  electronics
+description : An ML model will be implemented on an FPGA, specifically targeting the MNIST dataset. This model will inherently be accelerated on the FPGA.
+---
+## Blog
+
+ -- Atharv Patil :  https://github.com/Atharv1035 
+  I'm Atharv Patil from BTech Electronics . I opted for this project because of my interest and inclination towards the domains of ML and Embedded Systems & FPGA's. Neural Networks are fascinating for me and I aim to build a strong foundation through this project that would help me further.
+  In this project, I will be using FPGA's and neural networks to create a machine learning model that can distinguish between handwritten digits.
+
+ -- Soha Jawdekar : https://github.com/Sohajawdekar
+ABOUT MYSELF 
+I am Soha Jawdekar from electronics and telecommunication branch. I am always looking forward to new learning opportunities. I am looking forward to learning the practical side of electronics field , how the devices actually work, and can be modified to make them better. I am on a journey of exploring all the options and grabbing all the opportunities that I encounter, and make the best out of it.
+WHY I CHOSE THIS PROJECT
+I picked the ml model on fpga project out of all the fascinating ones. I found the reconfigurability and parallel processing of fpga to be really intriguing. Learning fpga programming is something I am eager to do. Although the brain is a tremendously potent instrument, it is extraordinary when identical networks and capabilities are developed in robots. I've always been curious in how something as highly functional as a human brain can be duplicated. With the aid of this project, I will be able to design a neural network from scratch and use it on an FPGA to learn how it functions.
+
+## Project Description & Domains
+
+### What we will be doing in this project
+- __DESCRIPTION__ : An ML model will be implemented on an FPGA, specifically targeting the MNIST(**Modified National Institute of Standards and Technology database**) dataset. This model will inherently be accelerated on the FPGA.
+- The MNIST dataset consists of a large database of handwritten digits that is commonly used for training various image processing systems.
+- We will be creating an ML model and train it to accurately detect any number from the MNIST dataset on an FPGA.
+### What is an FPGA & how do they work ?
+-  Field Programmable Gate Arrays
+-  It is an Integrated Circuit (IC)  that contains an array of reprogammable logic gates/components
+-  Conventional IC's like microcontrollers and microprocessors have a fixed digital circuit design/structure that cannot be modified ; all modifications are possible only at the software level
+- FPGA's , on the other hand are made up of CLB(Configurable Logic Blocks) that can be reprogrammed as per the user requirements.
+- FPGA's are programmed using Hardware Description Languages (HDL's) . Two of the most commonly used HDL's are Verilog HDL and VHDL .
+-  HDL's describe the basic layout/design of a circuit /circuit element ranging from basic wires & logic gates to flip-flops and multiplexers(mux).
+- ![](/assets/posts/ML_model_on_FPGA/FPGA vs MCU fig1.png)
+
+### What is Machine Learning ?
+- Machine Learning is a branch of AI that aims to study and imitate human learning patterns using data sets and algorithms.
+- Instead of being explicitly programmed to perform a specific task, machine learning systems use patterns and data to improve their performance over time.
+
+### What are Neural Networks ?
+- Neural networks are computational models inspired by the structure and functioning of the human brain's interconnected neurons.
+- The basic components of a neural network include:
+	1. **Input Layer:** This layer receives the initial data or features that are used as input to the network.    
+	2. **Hidden Layers:** These layers are intermediate layers between the input and output layers. Each neuron in a hidden layer performs a weighted sum of its inputs, applies an activation function, and produces an output that serves as the input for the next layer.
+	3. **Output Layer:** This layer produces the final output of the network, which can be in the form of predictions, classifications, or any other desired output.
+- ![](/assets/posts/ML_model_on_FPGA/Neural_Network.jpg)
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