PrajnaEdge is not navigated like a conventional website. Knowledge is represented through paths, structures, and transitions.
Matter
NodeClick a node name to enter that concept and continue along the awakened path.
ExplorationsStep-by-step learning experiences containing theory, architecture, and interactive elements.
SetuA Setu connects related concepts that can be explored independently from the main sequence.
VortexA Vortex opens a path toward another knowledge area, foundation, or future domain.
BlackholeA deeper exploration space opened through the Vortex, where you select the depth of investigation.
AntarguhaA concealed exploration existing outside the regular path, waiting to be discovered within the system.
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PrajnaEdge
A curiosphere for curious minds who want to understand, experiment with, and experience technology.
Technology is a system of connections.
Modern technology is built from layers that continuously interact with one another. At the physical level, electronic devices transform electrical signals into digital information. Digital logic turns that information into computation, while processors, memory and communication interfaces provide the machinery needed to execute instructions and move data. As these components become part of embedded systems, they begin to interact with the physical world through sensors, controllers, actuators and real-time software.
But computation does not exist in isolation. Operating systems coordinate hardware and software, firmware gives specialized machines their behaviour, and communication protocols allow independent systems to exchange information. At the same time, machine learning is moving beyond the cloud into edge and on-device systems, where models must operate within real constraints such as memory, processing power, latency and energy consumption.
PrajnaEdge explores these connections as one continuous technology landscape — and takes them beyond explanation. From computing foundations and embedded systems to intelligent machines and edge AI, ideas can be understood, experimented with, and eventually turned into technology that can be experienced in the real world.
PrajnaEdge products and technology experiences are currently in development.
In Development
Playground
Experiment with intelligence beyond the cloud.
AI inference performed closer to where data is generated — optimizing model representation to reduce latency, memory footprint, and bandwidth requirements.
Run an optimized, trained model directly on physical embedded silicon.
Cloud AIComing Soon
Model Training & Representation
Explore how neural networks learn representations from data before compression and deployment. Future experiments will allow adjusting learning parameters—such as learning rate, epoch counts, and batch sizes—to observe loss trajectories and decision boundaries.
Edge AIComputer Vision
Image Classification
Can this image classifier maintain its intelligence while becoming small enough for the edge?
On-device AIAI-Thinker ESP32-CAM
On-device Image Classification
Run a fully quantized INT8 image-classification model directly on embedded hardware.
On-device AIComing Soon
On-Device Model Adaptation
Can a constrained edge device adapt or fine-tune model parameters directly using local data? Exploring on-device transfer learning and few-shot adaptation under strict compute, memory, and thermal limits.
Edge AI Playground
Image Classification
Can this image classifier maintain its intelligence while becoming small enough for the edge?
Choose an image
Upload an image
Supports JPG, JPEG, PNG
This classifier recognizes only Apple, Banana, and Orange. Other objects may be incorrectly classified as one of these classes.
Run a fully quantized INT8 image-classification model directly on embedded hardware.
This experiment investigates preparing a compact INT8 TensorFlow Lite model for direct inference on resource-constrained embedded hardware. The model uses the same CNN architecture as the Edge AI exploration, converted to a full INT8 representation to classify three classes: Apple, Banana, and Orange.
Practical guides to embedded systems and engineering.
Automotive · EmbeddedProtocol Debugging
CAN Bus Debugging: From Symptoms to Root Cause
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Edge AI Demonstrations
Deploying neural networks and intelligent decision loops on raw silicon targets.
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Operating Systems
When One Rule Was Enough
Understanding Non-Preemptive Scheduling
KernelSchedulingNon-PreemptiveFCFSSJFPriority
The Non-Preemptive Execution Flow
To visualize this philosophy, consider the lifecycle of execution. Once a process is selected from the Ready Queue, it enters the running state and remains there. The CPU cannot swap it out mid-way.
Three Classic Non-Preemptive Policies
When the kernel does not interrupt, the only choice it makes is who starts next. This choice is guided by one of three classic rules:
* First-Come, First-Served (FCFS): The simplest possible rule. Whichever process enters the Ready Queue first is run first, regardless of how long it takes or its importance.
* Shortest Job First (SJF): The scheduler searches the Ready Queue and executes the process with the shortest estimated burst time first.
* Priority Scheduling: Each process is tagged with an integer representing its priority. The scheduler always starts the process with the highest priority next.
Investigating Scheduling Behavior
To see how these rules change the behavior of a system, let's trace them using a single, identical set of processes.
The Key Metrics: Turnaround vs. Waiting
When evaluating these schedulers, engineers look at two primary performance metrics:
* Turnaround Time (TAT): The total elapsed time from when a process first arrives in the queue to when it completely finishes execution (TAT = Completion Time - Arrival Time).
* Waiting Time (WT): The total time a process spends sitting in the Ready Queue waiting to execute (WT = Turnaround Time - Burst Time).
As you simulated above:
* FCFS scheduled processes exactly in arrival sequence, resulting in an average waiting time of 4.67 units.
* SJF reduced average waiting time to 3.67 units by running the shorter P3 before P2.
* Priority prioritized the urgent P2 over P3, resulting in an average waiting time of 4.67 units.
The EdgeCase: Emergency Arrival
These non-preemptive rules worked well for early batch-processing computers. But what happens in a real-time system when an critical task suddenly wakes up?
Select the EdgeCase simulation tab above. In this scenario, we use the exact same process table, but we introduce one addition: at Time = 1, an emergency process (PEM) with priority 0 (highest) arrives.
Observe the timeline:
* At Time = 0, P1 begins running.
* At Time = 1, PEM arrives. It is the most critical process in the system.
* But because the scheduler is non-preemptive, P1 cannot be interrupted. PEM is forced to wait in the Ready Queue for 5 full time units until P1 finishes at Time = 6.
This is the fundamental limitation of non-preemptive scheduling: long-running tasks block critical work, and the kernel is powerless to stop them.
The early policies were simple and predictable. But when a system must react to user inputs or critical hardware interrupts, waiting is no longer an option.
Should the CPU continue executing a long task, or should it force it to yield?
Let's find out how the kernel breaks the rule of non-preemption.
Engineering concepts you don't just read — you experience.
Founded in 2026.
PrajnaEdge is a technology company exploring the space between understanding technology, experimenting with ideas, and turning them into things that can be experienced.
Our Mission
To make technology easier to explore, deeper to understand, and more exciting to experience.
Our Vision
To build a technology ecosystem where curiosity, experimentation and creation continuously lead to one another.
Where it began
Embedded Systems
PrajnaEdge began with Embedded Systems — exploring the foundations that connect hardware, software and intelligent computation.
The first technology universe is built around that foundation. The journey will expand as new ideas, experiments and products emerge.
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 PHILOSOPHY
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.
Where it all began — understanding the physical layer of computation. Circuits, signals, and systems gave me a mental model of how information moves through hardware.
⬡
Connects to Systems Understanding circuits directly enables writing firmware that talks to peripherals at the register level.
What it is
BTech in Electronics and Communication
Undergraduate foundation covering analog & digital circuits, signal processing, microprocessors, and communication systems.
CircuitsSignal ProcessingMicroprocessorsVLSI
What I did
Core Engineering Fundamentals
Studied semiconductor physics, digital logic design, and embedded microcontrollers. Built prototypes using 8-bit MCUs.
8051Logic DesignPCB Basics
What I learned
The Hardware Mental Model
Every software abstraction sits on physical reality. Understanding silicon teaches you why timing, power, and noise are first-class engineering problems.
Let's Connect
Interested in embedded systems, AI, or building something meaningful? I'd love to hear from you.
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Legal & Privacy
Last updated: September 2026
1. Terms of Use
Welcome to PrajnaEdge (prajnaedge.dev), an independent engineering and technology platform created and maintained by Devaharsha Meesarapu. By using this website, you agree to these terms.
Educational & Research Focus: PrajnaEdge publishes interactive technical explorations, architectural models, and simulation walk-throughs covering embedded systems, computer architecture, operating systems, and edge artificial intelligence. All materials, interactive tools, and code demonstrations are provided solely for educational and conceptual understanding.
Hardware & Firmware Disclaimer: Embedded programming interacts directly with hardware registers, physical voltages, and precise timing. While all writeups and code demonstrations are prepared with care, they are provided "as is" without warranty of any kind. You are responsible for reviewing component datasheets, circuit schematics, and electrical ratings before deploying code to physical microcontrollers or custom hardware.
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