u/microscammer

I'm an ECE student and I've recently started learning STM32 (Blue Pill / STM32F103). My goal right now is not RTOS, Linux, AI, or advanced projects. I just want to learn how to properly understand a microcontroller and write code from the reference manual.

The problem I'm facing is that I still don't fully understand even the basics. I can't confidently blink an LED on my own yet without following a tutorial step-by-step. I get confused about things like enabling clocks, configuring GPIO registers, and writing the correct bit manipulations. I feel like I'm missing some very fundamental understanding, and I want to build that from the ground up instead of just copying code.

I don't want to become someone who only copies HAL examples. I want to understand:

• How experienced embedded engineers read reference manuals
• What information should become muscle memory vs what should be looked up
• How to navigate a datasheet/reference manual efficiently
• How to go from "I want to blink an LED" to finding the right registers and writing the code myself
• How to learn bare-metal programming the right way

Could you recommend:

• YouTube channels
• Blog series
• STM32 learning paths
• Exercises/projects that specifically teach reading documentation and writing code from it

If you were starting from scratch today and your goal was to become very comfortable with microcontroller architecture, registers, datasheets, and bare-metal programming, how would you do it?

I'd appreciate any roadmap or resources from people who have gone through this themselves.

I'm an ECE student and I've recently started learning STM32 (Blue Pill / STM32F103). My goal right now is not RTOS, Linux, AI, or advanced projects. I just want to learn how to properly understand a microcontroller and write code from the reference manual.

The problem I'm facing is that I still don't fully understand even the basics. I can't confidently blink an LED on my own yet without following a tutorial step-by-step. I get confused about things like enabling clocks, configuring GPIO registers, and writing the correct bit manipulations. I feel like I'm missing some very fundamental understanding, and I want to build that from the ground up instead of just copying code.

I don't want to become someone who only copies HAL examples. I want to understand:

• How experienced embedded engineers read reference manuals
• What information should become muscle memory vs what should be looked up
• How to navigate a datasheet/reference manual efficiently
• How to go from "I want to blink an LED" to finding the right registers and writing the code myself
• How to learn bare-metal programming the right way

Could you recommend:

• YouTube channels
• Blog series
• STM32 learning paths
• Exercises/projects that specifically teach reading documentation and writing code from it

If you were starting from scratch today and your goal was to become very comfortable with microcontroller architecture, registers, datasheets, and bare-metal programming, how would you do it?

I'd appreciate any roadmap or resources from people who have gone through this themselves.

I'm an ECE student primarily interested in embedded systems, understanding how microcontrollers and digital hardware work internally, and eventually deploying CNN/TinyML models on resource-constrained hardware (STM32, ESP32, Cortex-M class devices).

​

I need to choose one of the following electives:

​

Digital System Design

Linear Integrated Circuits

Chip Level Photonics

Nano Electronics

Digital Image Processing

Speech and Natural Language Processing

​

For context, I'm more interested in:

Firmware and RTOS development

Microcontroller architecture

Registers, peripherals, interrupts

Embedded AI / Edge AI

Optimizing ML models for constrained hardware

I'm not specifically targeting VLSI, semiconductor fabrication, or photonics research.

Which elective would provide the most long-term value for this path, and how would you rank the others?

Thanks!

I’m an ECE student trying to figure out the best direction for my capstone/final year project and career path, and I’d really appreciate advice from people working in embedded systems / Edge AI / TinyML.

Recently I got very interested in running ML models on constrained hardware like STM32/ESP32. Initially I was thinking of doing a small project like handwritten digit recognition using a CNN on STM32 as a summer project so I can understand the complete pipeline:

• training a tiny CNN
• quantization/fixed-point
• deployment on embedded hardware
• inference optimization
• memory/latency constraints
• CMSIS-NN / ARM optimization

What really interests me is NOT just “using AI”, but understanding how inference actually runs efficiently on constrained systems.

One of my friends is building an FPGA-based CNN accelerator using systolic arrays and custom RTL, which made me realize I’m more interested in the embedded AI systems/runtime/software side rather than HDL-heavy hardware design. I enjoy C much more than Verilog/VHDL and I also want to learn low-level optimization + assembly.

Long term, I want my capstone project to be something deep and industry-relevant in Edge AI/Embedded AI — not just a basic demo project. I want something that:

• teaches real engineering skills companies care about
• has enough technical depth for a paper/research-style writeup
• helps me understand AI inference/optimization deeply
• could potentially stand out for roles in embedded AI / Edge AI / systems engineering

Right now I’m considering directions like:

• optimized CNN inference runtime on STM32
• fixed-point/quantized inference engine
• ARM Cortex-M optimization
• TinyML framework/runtime ideas
• real-time embedded vision systems

My questions are:

1. Is STM32 the right platform to go deep into this field?
2. What kind of projects in embedded AI actually stand out technically?
3. What do companies working in Edge AI/embedded ML actually value?
4. What skills should I focus on if I want to work on serious Edge AI systems in the future?
5. Is building custom inference/runtime optimizations a good capstone direction?

Would appreciate brutally honest advice from people in industry/research. I’m trying to avoid shallow “AI demo” projects and instead build strong fundamentals in a niche that has long-term value.

I’m an ECE student trying to figure out the best direction for my capstone/final year project and career path, and I’d really appreciate advice from people working in embedded systems / Edge AI / TinyML.

Recently I got very interested in running ML models on constrained hardware like STM32/ESP32. Initially I was thinking of doing a small project like handwritten digit recognition using a CNN on STM32 as a summer project so I can understand the complete pipeline:

• training a tiny CNN
• quantization/fixed-point
• deployment on embedded hardware
• inference optimization
• memory/latency constraints
• CMSIS-NN / ARM optimization

What really interests me is NOT just “using AI”, but understanding how inference actually runs efficiently on constrained systems.

One of my friends is building an FPGA-based CNN accelerator using systolic arrays and custom RTL, which made me realize I’m more interested in the embedded AI systems/runtime/software side rather than HDL-heavy hardware design. I enjoy C much more than Verilog/VHDL and I also want to learn low-level optimization + assembly.

Long term, I want my capstone project to be something deep and industry-relevant in Edge AI/Embedded AI — not just a basic demo project. I want something that:

• teaches real engineering skills companies care about
• has enough technical depth for a paper/research-style writeup
• helps me understand AI inference/optimization deeply
• could potentially stand out for roles in embedded AI / Edge AI / systems engineering

Right now I’m considering directions like:

• optimized CNN inference runtime on STM32
• fixed-point/quantized inference engine
• ARM Cortex-M optimization
• TinyML framework/runtime ideas
• real-time embedded vision systems

My questions are:

1. Is STM32 the right platform to go deep into this field?
2. What kind of projects in embedded AI actually stand out technically?
3. What do companies working in Edge AI/embedded ML actually value?
4. What skills should I focus on if I want to work on serious Edge AI systems in the future?
5. Is building custom inference/runtime optimizations a good capstone direction?

Would appreciate brutally honest advice from people in industry/research. I’m trying to avoid shallow “AI demo” projects and instead build strong fundamentals in a niche that has long-term value.

I’m an ECE student trying to figure out the best direction for my capstone/final year project and career path, and I’d really appreciate advice from people working in embedded systems / Edge AI / TinyML.

Recently I got very interested in running ML models on constrained hardware like STM32/ESP32. Initially I was thinking of doing a small project like handwritten digit recognition using a CNN on STM32 as a summer project so I can understand the complete pipeline:

• training a tiny CNN
• quantization/fixed-point
• deployment on embedded hardware
• inference optimization
• memory/latency constraints
• CMSIS-NN / ARM optimization

What really interests me is NOT just “using AI”, but understanding how inference actually runs efficiently on constrained systems.

One of my friends is building an FPGA-based CNN accelerator using systolic arrays and custom RTL, which made me realize I’m more interested in the embedded AI systems/runtime/software side rather than HDL-heavy hardware design. I enjoy C much more than Verilog/VHDL and I also want to learn low-level optimization + assembly.

Long term, I want my capstone project to be something deep and industry-relevant in Edge AI/Embedded AI — not just a basic demo project. I want something that:

• teaches real engineering skills companies care about
• has enough technical depth for a paper/research-style writeup
• helps me understand AI inference/optimization deeply
• could potentially stand out for roles in embedded AI / Edge AI / systems engineering

Right now I’m considering directions like:

• optimized CNN inference runtime on STM32
• fixed-point/quantized inference engine
• ARM Cortex-M optimization
• TinyML framework/runtime ideas
• real-time embedded vision systems

My questions are:

1. Is STM32 the right platform to go deep into this field?
2. What kind of projects in embedded AI actually stand out technically?
3. What do companies working in Edge AI/embedded ML actually value?
4. What skills should I focus on if I want to work on serious Edge AI systems in the future?
5. Is building custom inference/runtime optimizations a good capstone direction?

Would appreciate brutally honest advice from people in industry/research. I’m trying to avoid shallow “AI demo” projects and instead build strong fundamentals in a niche that has long-term value.

the entitlement some North Indians bring to PES needs to be studied , you came to Karnataka for a degree and placements, not to build your own illiterate Hindi bubble

Every week it’s the same dumbass questions:
“Will people know Hindi?”
“Is there language imposition?”
“What’s the North to South ratio?”

Like are you serious 😭 . Learn to speak proper English first before expecting an entire state to cater to your Hindi bubble.

And some of these mfs will reply in Hindi even when I start the convo in English the entitlement is insane. They walk into another state and still expect everyone else to adjust to them. You came to Karnataka. People here are gonna speak Kannada. Deal with it.

Note: this isn’t a hate post nobody cares where you’re from. The problem is the entitlement of coming to another state and expecting everyone to know Hindi and adjust to you. Learn English..

I’m a 19-year-old BTech student (currently in my 2nd year, ECE at PESU) looking for a summer internship opportunity in Bengaluru, preferably in Edge AI / embedded AI systems.

I’m interested in learning:

How AI models run on edge devices (currently a beginner)

Embedded systems + ML integration from scratch

Real-time applications like IoT and smart systems

My skills and knowledge include:

C and Python programming

ESP32 (ESP-IDF)

OpenCV (worked on basic facial recognition)

Basic ML concepts (CNNs, regression, classification, etc.)

I’m comfortable with showing up on-site every day and would actually prefer that kind of hands on environment over remote work.

I’m open to startups, labs, or teams where I can learn, contribute, and gain real practical experience.
Thank you : )