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.

To continue exploring
Products

Technology, made tangible.

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.
Cloud AI Coming 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 AI Computer Vision

Image Classification

Can this image classifier maintain its intelligence while becoming small enough for the edge?

On-device AI AI-Thinker ESP32-CAM

On-device Image Classification

Run a fully quantized INT8 image-classification model directly on embedded hardware.

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.

Choose the model

Model size
4.91 MiB
Largest activation
~625 KiB
Test accuracy
99.11%
Measured model accuracy
Your image is processed locally in your browser.
On-device AI · Inference

On-device Image Classification

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.

Model size
1.23 MiB
INT8 quantized weights
Model accuracy
99.11%
Measured test accuracy
Target hardware
AI-Thinker ESP32-CAM
Embedded microcontroller target

Explore the ideas, systems and connections that shape technology — choose any node to begin your journey.

PrajnaEdge Navigation Tree
Embedded Systems Tree
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PrajnaEdge Library

PrajnaEdge Library

Practical guides to embedded systems and engineering.

CAN Bus Debugging: From Symptoms to Root Cause Book Cover
Automotive · Embedded Protocol 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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Computation

The Architecture of Memory

How physical constraints and electrical charge define the boundaries of digital state.

MemorySRAMDRAMFlashEmbedded Systems

1. The Hierarchy of State

Every processor is fundamentally a state machine. It executes instructions by reading data from memory, transforming it, and writing it back. But not all memory is created equal. The physical reality of silicon forces a compromise between speed, capacity, and cost.

To build a useful system, we construct a hierarchy: registers and SRAM at the very top (tapering down near the CPU core for speed), backed by main DRAM system memory, and finally persistent NOR and NAND flash for mass storage. This tiered architecture ensures the CPU is never starved of instructions while keeping persistent data accessible.

The Memory Hierarchy showing speed vs density trade-offs
The Memory Hierarchy showing speed vs density trade-offs

A vertical structural view of memory layers tapering down as they approach the CPU core.

2. The Battle for the Bit: SRAM vs DRAM

At the volatile layer, the choice comes down to topology: how do we store a single bit of data?

Static RAM (SRAM) uses a cross-coupled latch constructed with 6 transistors (6T). This design creates an active, stable state that holds its value as long as power is applied. However, this complexity makes SRAM physically large and expensive, limiting its use to small, fast CPU caches and microcontroller registers.

Dynamic RAM (DRAM) takes the opposite approach. It shrinks the cell down to a single transistor and a single storage capacitor (1T1C). While this allows massive densities (gigabytes on a single chip), the capacitor is a leaky reservoir. It naturally drains its charge within milliseconds, requiring a continuous refresh loop to prevent data corruption.

SRAM 6T vs DRAM 1T1C circuit diagram
SRAM 6T vs DRAM 1T1C circuit diagram

Circuit topologies representing the active stable latch of SRAM versus the leaky reservoir of DRAM.

3. Persistence: NOR vs NAND Flash

When power is removed, we rely on non-volatile flash memory to retain our programs and data. Flash operates by trapping charge within a floating gate transistor. The layout configuration of these gates determines how they can be accessed.

NOR Flash connects storage transistors in parallel directly across word lines. This parallel alignment allows the processor to perform instant random byte reads, making it the standard choice for executing firmware binary code directly on-board.

NAND Flash daisy-chains transistor arrays in a tight serial configuration. By eliminating the individual contacts needed for parallel routing, NAND achieves extraordinary densities. However, this serial chain prevents byte-level access, requiring the processor to read and write data in sequential sector blocks, ideal for mass storage filesystems.

NOR parallel vs NAND serial micro-architectural layouts
NOR parallel vs NAND serial micro-architectural layouts

Transistor cell layout detailing parallel random-access lines in NOR vs serial block lines in NAND.

4. The Address Map: Dividing Space

To the developer, all of these physical layers are abstracted into a single, contiguous address space. The microcontroller registers map specific memory regions to Flash and SRAM boundaries.

From top to bottom, the register space is divided into proportional segments: .text (NOR Flash for execution), .data and .bss (RAM for initialized and zeroed variables), followed by the Stack (runtime context engine) and the Heap (dynamic allocation workspace).

Microcontroller memory map segment boundaries
Microcontroller memory map segment boundaries

The vertical register allocation of persistent Flash versus internal SRAM workspace segments.

The Architecture of State

Memory is not just a passive buffer; it is the physical medium where logic meets reality. The boundaries of digital state are defined by the speeds, densities, and physical layout structures of silicon.

Understanding these hardware constraints is what allows us to write firmware that pushes performance to the very edge.

True efficiency is achieved when you align your software execution path with the physical layout of the silicon.
System Tree Node Computation

PrajnaEdge

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.

PrajnaEdge is a technology company created by Devaharsha Meesarapu.

CREATOR PROFILE

Devaharsha Meesarapu

Embedded Systems • Firmware • Edge AI

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.

View Resume →

ABOUT ME

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.

CONNECT

LinkedIn → GitHub →

Interactive Career Journey

BTech · ECE

Foundations

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.
Open to collaborations, research, and interesting engineering conversations.
Help Improve PrajnaEdge
Found something to improve? I'd love to hear your thoughts.

Bare Metal

Software that runs directly on hardware without an operating system.

Applications
↑
Operating Systems
YOU ARE HERE
Bare Metal
Processor
↑
Hardware

"Every embedded application begins long before main()."

Operating Systems

An Operating System manages hardware and software resources so complex applications can work efficiently.

Applications
↑
YOU ARE HERE
Operating Systems
Bare Metal
↑
Processor
↑
Hardware

"When one loop is no longer enough to carry the burden."

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Select Domain
Explore application domains branching from the Systems Tree
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← Return to Systems Tree
Select Depth
Examine computation through architectural depth layers
Architecture
Controller
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Programming
Processor
Explore Processors → Explore Controllers →

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