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Anudeepreddynarala/README.md

👋 Hi, I'm Anudeep Narala

LinkedIn Email

🔧 Embedded Systems Engineer

Building firmware solutions for MCU platforms | Passionate about RTOS development, bare-metal programming, and edge AI

I specialize in developing embedded systems from hardware to software, with expertise in real-time operating systems, microcontroller programming, FPGA design, and deploying machine learning models on resource-constrained devices.

🛠️ Core Technologies

Embedded Systems & Hardware

C C++ Verilog ARM ESP32 FPGA

Development Tools & Frameworks

RTOS PlatformIO Keil Vivado Altium

Edge AI & ML

TensorFlow Lite TinyML YOLO Jetson

Software & Scripting

Python Rust JavaScript Git

🚀 Featured Embedded Projects

🔹 Real-Time Object Detection with Rust Networking

High-performance object detection pipeline combining TensorRT-optimized YOLO inference with low-latency Rust networking on NVIDIA Jetson Orin Nano. Features Unix socket IPC for inter-process communication and concurrent UDP/TCP streaming.

  • Tech: TensorRT FP16, Rust (Tokio async), Python, CUDA, Unix Sockets
  • Performance: 248.7 qps throughput, 4.6ms inference latency, <50ms end-to-end
  • Architecture: Parallel pipeline (Camera → TensorRT → Rust → UDP/TCP streams)

🔹 Resource-Constrained Edge ML Deployment

Ported TensorFlow Lite Micro to ESP32 with custom porting layer, resolving CMSIS-DSP incompatibilities. Achieved production-grade inference performance through multi-core task scheduling and DMA optimization.

  • Tech: ESP-IDF, TensorFlow Lite, CMSIS, CMake Build Systems, C/C++, FreeRTOS
  • Performance: 55.1ms inference, 85KB RAM, 6.2 inferences/sec, 342KB total firmware

🔹 Single-Precision Floating-Point FPGA

Implemented IEEE 754 single-precision floating-point arithmetic unit in Verilog. Hardware-accelerated computation for DSP applications.

  • Tech: Verilog, Xilinx Vivado, FPGA
  • Features: Addition, multiplication, division with pipelining

🔹 Stepper Motor Control (Verilog)

FSM-based stepper motor controller with configurable speed and direction control. Synthesized for FPGA deployment.

  • Tech: Verilog, FPGA, PWM, FSM design
  • Application: Robotics, CNC machines, automation

🔹 Manufacturing Test Station

Automated test fixture for embedded hardware validation. Interfaces with multiple sensors and actuators for production testing.

  • Tech: STM32, Python, I2C/SPI/UART protocols
  • Features: Automated pass/fail criteria, data logging

🔹 Semiconductor Yield Analyzer

Analytics dashboard for semiconductor manufacturing yield optimization. Real-time monitoring of hardware test results.

  • Tech: Python, Pandas, Plotly, embedded data collection
  • Application: Quality control, defect analysis

🎯 Current Focus

  • 🔬 Building TinyML models for ultra-low-power microcontrollers
  • ⚡ Optimizing real-time inference on edge devices (ESP32, Jetson)
  • 🛠️ Designing custom FPGA IP cores for signal processing
  • 📡 Developing IoT sensor networks with LoRa/BLE connectivity
  • 🤖 Integrating AI/ML into embedded robotics applications

📫 Let's Connect

I'm always excited to discuss embedded systems, hardware design, and edge AI projects!

Pinned Loading

  1. jetson-arcface-recognition jetson-arcface-recognition Public

    Real-time face recognition on NVIDIA Jetson using YOLO11 detection and ArcFace embeddings with encrypted storage

    Python

  2. manufacturing-test-station manufacturing-test-station Public

    Modular Manufacturing Test Station using OpenHTF for hardware validation and DUT testing

    Python

  3. semiconductor-yield-analyzer semiconductor-yield-analyzer Public

    Yield Analysis Tool using Isolation Forest ML algorithms for parametric test data and defect detection

    Python

  4. single-precision-floating-point-fpga single-precision-floating-point-fpga Public

    IEEE 754 single-precision floating-point calculator implemented in VHDL on Xilinx Cmod A7-35T FPGA with MATLAB GUI interface

    VHDL

  5. Stepper_motor_control_verilog Stepper_motor_control_verilog Public

    Verilog

  6. tinyml-esp32-inference tinyml-esp32-inference Public

    TinyML deployment system for ESP32 microcontrollers using TensorFlow Lite and Edge Impulse SDK

    C++