Exploring how systems evolve from hardware to integration.
Deploying neural networks and intelligent decision loops on raw silicon targets.
How to squeeze a real gesture-recognition algorithm inside an Arduino Uno with only 2KB of RAM.
When we talk about Edge AI, we usually think of powerful chips, massive neural networks, and gigabytes of memory. But what happens when your target is a basic Arduino Uno running an 8-bit ATmega328P processor?
You get exactly 2 Kilobytes of SRAM. If your code uses even a byte more, the stack crashes, variables corrupt, and the system resets. On this scale, running a heavy AI framework like TensorFlow Lite is out of the question. You have to build smart, efficient code by hand.
For this setup, I wired an MPU6050 accelerometer and gyroscope to the Uno using the I2C pins (A4 and A5). The sensor continuously measures acceleration and rotation along the X, Y, and Z axes.
Instead of saving hundreds of sensor readings into a massive array—which would instantly kill our 2KB RAM budget—we process the values on the fly. As soon as the raw bytes arrive over the I2C bus, we immediately convert them into basic features like averages, moving peaks, and signal direction.
The Arduino Uno does not have a hardware Floating Point Unit (FPU). Every time you use a decimal number (like 1.45 or -0.82), the processor has to emulate the math via software, which is incredibly slow and blows up your compiled file size.
To get around this, we use fixed-point quantization. Instead of working with decimals, we scale our values up into raw integers. For example, multiplying a threshold by 1000 lets us do all of our pattern checks using simple, lightning-fast integer logic.
Instead of a deep learning model, I trained a highly optimized Decision Tree on my computer using gesture data. Once the tree structure was ready, I exported its exact boundaries as plain C++ conditional logic blocks and small arrays.
The result is an automated processing path that checks raw signals against tight integer limits to map movements instantly.
if (gyroZ < -1500) return GESTURE_WAVE; } else { return GESTURE_IDLE; }
Because everything is written in clean, direct C++ code, the entire pattern-matching sequence runs in less than 4 milliseconds.
The model uses under 1.1KB of RAM, leaving plenty of room for stable system execution. It proves you don't need expensive hardware to create an intelligent, self-contained edge device.
PrajnaEdge is an interactive engineering platform where complex concepts become experiences—through visual explorations, simulations, and practical understanding.
Engineering is often taught as a collection of isolated concepts.
A processor here.
A protocol there.
An operating system somewhere else.
But real systems are built by connecting these layers.
PrajnaEdge exists to make those connections visible.
Each exploration starts with a question, builds an intuition, and gradually reveals the system underneath through visualizations, simulations, practical scenarios, and connections between concepts.
PrajnaEdge is designed around exploration rather than passive reading.
Concepts are introduced progressively, visualized when they benefit from seeing them, and brought to life through interactive EdgeCases and simulations where appropriate.
The goal is not simply to explain what a system does, but to help the learner understand why it works the way it does.
I am the engineer behind the design, development, and content of PrajnaEdge. I build low-level systems where code directly controls hardware, bridging the gap between register-level silicon behavior and intelligent edge decision loops.
I am an Embedded Firmware Engineer focused on developing software for resource-constrained systems. My experience spans bare-metal firmware, device drivers, microcontroller peripherals, and communication protocols, working across the boundary between hardware and software.
My work has involved microcontroller-based systems, real-time behaviour, hardware interfaces, and communication technologies such as CAN, CAN FD, UART, SPI, and I²C. I am particularly interested in understanding systems from the lowest level upward—from registers and peripherals to intelligent edge systems.
Engineering is not just about writing code; it is about managing constraints, timings, and physical hardware characteristics. True mastery of complex systems comes from understanding the interactions across different layers of the stack.
This conviction is why I built PrajnaEdge—to bridge the gap between conceptual theory and direct, register-level physical reality.
Software that runs directly on hardware without an operating system.
"Every embedded application begins long before main()."
An Operating System manages hardware and software resources so complex applications can work efficiently.
"When one loop is no longer enough to carry the burden."
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PrajnaEdge is an interactive learning platform designed for systems engineers, developers, and technology enthusiasts. The educational materials, simulation blocks, and visual code tracers are provided for instruction and concept validation. We make no warranty regarding their completeness or applicability to real-world industrial systems.
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