Multi Domain Bootcamp in VLSI, Embedded & Edge AI Engineering

Sequential Logic, FSM & RTL Best Practices

Topics

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• Sequential logic design using flip-flops
• FSM design: Mealy vs Moore
• State encoding techniques
• Safe FSM coding practices
• Avoiding latches and race conditions
• RTL coding guidelines

Hands-on

• Design FSM for a control application
• Simulate and debug FSM behavior
• Identify latch inference issues
• Review RTL for quality issues

Timing, Clocking & CDC Concepts

Topics

• Timing fundamentals: setup & hold time
• Clock skew and uncertainty
• Multi-clock designs
• Clock Domain Crossing (CDC) issues
• Synchronizers and handshake mechanisms
• Impact of RTL on timing closure

Hands-on

• Analyze timing paths conceptually
• Identify CDC issues in RTL examples
• Apply synchronizer concepts
• Interpret basic timing reports

Synthesis, Verification & Industry Practices

Topics

• RTL synthesis flow
• Mapping RTL to gates
• Area, timing, and power trade-offs
• Verification concepts: simulation & testbenches
• Linting, synthesis warnings, RTL checks
• Industry documentation & review practices

Hands-on

• Run synthesis (demo/concept)
• Analyze synthesis reports
• Debug common RTL mismatches
• Case discussion on real RTL failures

Analogue & Digital Electronics Foundations

Topics

• Overview of embedded systems and real-world applications
• Passive components: resistors, capacitors, inductors – characteristics & selection
• Circuit laws: KCL and KVL
• Diodes: PN junction, rectifiers, clipping & clamping
• Transistor basics: BJT construction, operating regions
• Digital electronics fundamentals: number systems, logic gates

Hands-on

• Identify components on a real circuit board
• Analyse a given circuit using KCL/KVL
• Rectifier circuit behaviour analysis
• Logic gate truth-table verification

Digital Design & Embedded Hardware Interfaces

Topics

• Boolean algebra & logic simplification
• Combinational circuits: adders, multiplexers, encoders
• Sequential circuits: latches, flip-flops, counters
• Embedded system architecture & block diagram
• Communication interfaces: UART, I2C, SPI
• Reading schematics & datasheets

Hands-on

• Simplify logic expressions using K-maps
• Design a basic combinational circuit
• Identify UART/I2C/SPI pins from a datasheet
• Interpret timing diagrams from datasheets

Embedded C Programming & Debugging

Topics

• Embedded C vs desktop C
• Memory architecture: flash, RAM, stack, heap
• Pointers, volatile, const, bitwise operations
• Register-level programming basics
• Bug types and debugging techniques

Hands-on

• Write Embedded C programs for GPIO control
• Bit manipulation using registers
• Debug a faulty C program using breakpoints
• Analyse stack and variable values using debugger

Embedded OS Concepts & Python for Embedded

Topics

• Bare-metal vs RTOS vs Embedded Linux
• Tasks, scheduling, interrupts, synchronization
• Common RTOS problems: deadlock, priority inversion
• Python basics for embedded engineers
• Python for automation and log analysis

Hands-on

• Analyse task execution using timing diagrams
• Identify race conditions from given scenarios
• Write Python scripts to parse embedded logs
• Automate a simple test case using Python

Embedded AI & Edge Intelligence – 4 Days

AI & Machine Learning Fundamentals

Topics

• AI vs ML vs Deep Learning
• Types of ML (Supervised, Unsupervised, Reinforcement)
• ML Workflow (Data → Training → Evaluation → Deployment)
• Key Terminologies (Overfitting, Bias-Variance, Hyperparameters, Loss)

Hands-on

• Build a simple classification model using Scikit-learn
• Data preprocessing (scaling, splitting)
• Evaluate using confusion matrix
• Explain ML workflow for an IoT use case

Classical ML Algorithms

Topics

• Regression (Linear Regression – core idea)
• Classification (Logistic Regression, KNN, Decision Tree)
• Model Evaluation Metrics (Accuracy, Precision, Recall, RMSE)
• Algorithm selection strategy

Hands-on

• Implement Linear & Logistic Regression
• Compare two classifiers on sample dataset
• Perform basic hyperparameter tuning
• Interview Q&A on when to use which algorithm

Deep Learning & ANN

Topics

• Need for Deep Learning
• Artificial Neuron & Perceptron
• ANN Architecture (Input, Hidden, Output layers)
• Activation functions & Loss functions
• Backpropagation (conceptual understanding)

Hands-on

• Build a simple ANN using TensorFlow/Keras
• Train & visualize loss vs epochs
• Modify activation functions and observe results
• Whiteboard explanation of backpropagation

Embedded AI & ESP32 Deployment

Topics

• Introduction to Embedded AI & Edge AI
• ESP32 architecture basics (relevant to AI deployment)
• Model conversion (TensorFlow Lite & Quantization)
• Deployment workflow on microcontroller
• Memory & latency constraints

Hands-on

• Train lightweight model
• Convert to TensorFlow Lite
• Quantize model
• Deploy on ESP32
• Run real-time inference demo