PG Diploma in Embedded AI with Robotics Applications (A Job-Oriented Training Program)

Durations – 500 Hrs.

Modules

Core Engineering

Digital Hardware Familiarization
Digital Electronics, Logical circuit design
Timing analysis

Core Programming Fundamentals.

Mastering in C & C++
Master in C Programming
Mastering OOP using C++

SPECIALIZATIONS:

VLSI Design

RTL Coding with Verilog
Digital circuits design with different modeling styles
On-Chip Protocols Design
FPGA Programming>

Verification:

Design and Verification using System Verilog
OOPs in System Verilog
Randomization & Constraints
Functional Coverage
Test bench development

Experiential Project Based Learning

A Prototype Digital System Design using RTL Modeling, FPGA Deployment, and System Verilog-Based Verification

Project stream:

Core Programming

Application development based on Data Structure (Eg: Multi Client Chat Application, memory Leak Detection tool kit, E-Commerce cart simulator)

VLSI

Design and Simulation of Digital Controller/CPU Core/Protocols such as UART, SPI, I2C, AXI4
Design, Simulation, Implementation and Verification of Digital Controllers, ALU Cores and protocols such as UART, SPI, I2C, AXI4 on FPGA Board.
SV Verification of Digital Controller/CPU Core/Protocols such as UART, SPI, I2C, AXI4

Platform:

XILINX VIVADO
Questasim/EDA Playground
Artix7 FPGA Board/ ZYNQ SOC Board

Curriculum Overview

Core Programming Fundamentals
Mastering C Programming – 40 hrs.
Introduction to C: Simple C program structure, Literals, constants, variables and	Operators with precedence and associativity	Control flow statements with Examples
Modular Programming using functions	Numeric Arrays: 1D and 2D arrays	Character Arrays, String functions
Searching algorithms	Sorting Algorithms	Problem Solving using Hacker rank
Data Structures and Algorithm for Problem Solving in C & Competitive problem solving – 60 hrs.
Time and Space Complexity	Utopian Tree	Viral Advertising
Birthday Cake Candles	Migratory Birds	Kaprekar Number
Pangram String and Anagram String	Palindrome Index	Array Rotation
Pointers: Declaration and Initialization, types, pointer to pointers	Structures: Definition, structure variable, member access, nested structures	Introduction to Data Structures: Stacks, Queues, Linked List
Dynamic Memory Allocation	Static Stack and Dynamic Stack	Static Queue and Dynamic Queue
Circular Queue	Linked List: Singly Linked List	Doubly Linked List
File Handling Using C	Trees, DFS, BFS	Graphs
Mastering OOP using C++ & Competitive problem solving – 54 hrs.
Basic input / output: Cin, cout, >> and << operators, endl, setw	Understanding namespace Introduction to Object-Oriented Programming	Classes and objects, Encapsulation, Data hiding, abstraction
Access Specifiers – Private and Protected, This pointer	Constructors and Destructors	Friend functions and operator overloading
Inheritance	Run time polymorphism	Exception Handling
Lambda Expression	Smart Pointers	Templates
STL	Problem Solving using Hacker rank	Project Work
Core Engineering
Electronics and Hardware Familiarization – 30 hrs.
Analog Electronics: Passive and Active components	Circuit analysis using KCL and KVL	Diode, Transistor and Op-amp Circuits
Digital Electronics: Combinational circuits design: Adders, Multiplexers, Encoders, Decoders	Sequential circuits design: Flipflops, Registers, Counters	Microprocessors and Microcontroller architecture
Basic Embedded System Architecture	Standard Interfaces	Understanding schematics/datasheet

Embedded Systems Programming and Real-Time Control
ARM Cortex-M Architecture with Embedded C Programming – 40 hrs.
ARM Cortex-M3 Architecture & LPC1768 Overview	GPIO Registers, GPIO Programming: LED Programming	buzzer and switch programming IO device programming: 16 x 2 LCD interfacing and programming
4X4 matrix keypad Interfacing and programming	ADC Programing: LM35 temperature sensor interfacing and programming	Timer Peripheral Programming
ARM Cortex-M3 Architecture & LPC1768 Overview	GPIO Registers, GPIO Programming: LED Programming	buzzer and switch programming IO device programming: 16 x 2 LCD interfacing and programming
Embedded Protocols and Driver Development – 40 hrs.
PWM peripheral Programming	RTC (Real-Time Clock)	Watchdog Timer (WDT)
PLL (Phase-Locked Loop) & Clock Configuration	NVIC (Nested Vectored Interrupt Controller) & Interrupt Handling	UART (Universal Asynchronous Receiver Transmitter) Communication
SPI (Serial Peripheral Interface) Communication	SSP (Synchronous Serial Peripheral) Communication	I2C (Inter-Integrated Circuit) Communication
Linux System Programming – 30 hrs.
Linux Shell Commands	Manipulating files and directories	Manipulating data
File Related System Calls	Process Management	Signal
IPC – Pipes, Message Queue, Shared Mem	Multithreading	Handling Race Condition using Mutex
Operating Systems
Linux Device Driver & kernel – 40 hrs.
Introduction to kernel programming	Makefile	Simple kernel module
Kernel dependency module using EXPORT_SYMBOL and extern	Passing parameters to the kernel module.	Introduction to device drivers.
Character device driver and real device driver.	Major and minor numbers.	Real character device driver.
USB device driver.
Embedded RTOS (Free RTOS) Firmware Programming – 20 hrs.
Overview of FreeRTOS: Features of freeRTOS, FreeRTOS source code organization	RTOS Concepts: Hard real time vs soft real time, Multi-threading/ Multi-tasking / Concurrent execution	Scheduling and Context switching
Memory management: Heap vs Stack memory, program memory vs data memory	freeRTOS Heap Memory Management, different memory allocation schemes free RTOS Heap Utility Functions, Optimizing memory	Concept of freeRTOS Tasks freeRTOS Tasks APIs, Creating Tasks, Task Priorities, Task State Transitions
Scheduler: Scheduler Algorithms, Tick Interrupt, Idle task	Inter task Communication and synchronization: freeRTOS Queue APIs Data storage for Queue	Blocking read, write Receiving data from multiple queues Mailbox (using queue)
Interrupt Management: Events and ISRs, Tasks vs ISRs	Semaphores: Concept of semaphores, Binary Semaphores, Counting semaphores	Resource Management: Shared resources. Mutual Exclusion,

AIML
Machine Learning
Machine Learning Fundamentals & Advanced ML
Introduction to Machine Learning	Regression	Logistic regression
Supervised machine learning	Simple linear regression	Naïve Bayes Classification
Unsupervised machine learning	Multiple linear regression	Decision tress and its types
Train test split the data	Performance measure for regression	K Nearest Neighbor Classification
ML Workflow for project implementation	Classification and types	Performance Measure for Classification
Random Forest,	Clustering and types	Evaluate clustering resultn8s, Elbow Plot
Optimizing regression models with forward elimination, grid search cv	Improving classification models with Ensemble modeling	Model evaluation strategies (KFold, Stratified KFold)
Regularization L1 and L2 regularization	Bagging	Boosting techniques: ADA boost
Hyperparameter Tuning, SVM	Stacking and Voting	Dimensionality Reduction with PCA
Introduction to Machine Learning	Regression	Logistic regression
Deep Learning using TensorFlow – 24 hrs.
What is Deep Learning	Performance measure for ANN	Building project based on CNN
Deep Learning Methods	Need for Hardware's in Deep Learning	Need for Data augmentation
Deep Learning Application	Basics of image processing	Batch Normalization, dropout
Artificial Neural Network	Opencv library	Object detection with CNN
Hidden Layers	Image reading, writing, enhancement	Object recognition with CNN
Activation Function	Edge detection, filtering, morphology	Forward and Backward propagation
CNN for computer vision	CNN architecture and its types	TensorFlow, PyTorch, Kera's
Recurrent Neural Network (RNN)	Long- Short term Memory (LSTM)	Basic Open CV Functions
Natural Language Processing – 24 hrs.
Introduction to NLP	NLP: Areas of Application	Understanding the Text
Text Encoding	Word frequencies and stop words	Bag of words representation
Stemming and Lemmatization	TF- IDF representation	Canonicalization
Phonetic Hashing	Spell Corrector	Point wise mutual Information
Gensim, Word2Vec	Word Embeddings	Named Entity Recognition (NER) and Parts of Speech Tagging
Dependency Parsing and Syntactic Analysis	Semantic Similarity and Sentence Embeddings	Bidirectional LSTM
Generative AI & Agentic AI – 30 hrs.
Introduction to Gen AI	Rule-based vs neural generation	Generative Adversarial Network
Variable Auto Encoder	Transformers	Application of Generative AI, Ethics
FastText and subword models	Sentence embeddings and similarity	Encoding long text documents
Visualizing embeddings with tools	Prompt Engineering	Zero – shot and few – shot prompts
Chain-of-thought prompting style	System and user prompts	Common prompt engineering mistakes