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Yashas
Kallambi

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Building at the
Edge of Hardware

I'm a Mechatronics Engineering student with a deep obsession for silicon, systems, and startups. My world lives at the crossroads of embedded hardware and machine intelligence — where circuits think and machines learn.

Currently diving into RISC-V architecture, exploring VLSI design flows, training ML models on edge devices, and building drone systems that push autonomy to its limits.

Beyond the lab bench, I'm a founder at heart — always prototyping ideas, talking to users, and moving fast.

RISC-V VLSI ML / AI Mechatronics Drones Startups Embedded C FPGA
0 Years in Tech
0 Projects Built
0 Certifications
0 Startups Founded

Experience &
Education

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Timeline

2026 — Present

B.E. Mechatronics Engineering

Specialising in embedded systems, control theory, and VLSI design. Maintaining high academic standing while founding side projects.

Jan 2023 - Present

Founder @ The Quantum Company.

Building autonomous drone systems for precision agriculture and surveillance. Handled hardware stack design and firmware development.

2023 - 2025

Foundation Preparations

Deep interest towards applied sciences and applied mathematics to hold a strong grip towards engineering.

2023

VLSI Design Workshop

Completed advanced RTL design and synthesis workshop covering Verilog, timing analysis, and ASIC flows.

Skills

Hardware
RISC-V / FPGA
Verilog / VHDL
PCB Design
Embedded C/C++
Software / AI
Python / TensorFlow
TinyML / Edge AI
CUDA Robotics

Certifications

VLSI Design FundamentalsCoursera / NPTEL
Supervised Machine Learning: Regression and Classification Stanford Online/ Coursera
Electronic Arts - Product Management Forage
RISC-V ArchitectureLinux Foundation
YK
Yashas K S yash13ks
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What I've
Been Building

RISC-V · Hardware

PulseCore CPU

A 5-stage pipelined RISC-V RV32I processor implemented in Verilog. Includes hazard detection, forwarding units, and a custom cache hierarchy. Synthesised and tested on Xilinx FPGA.

VerilogXilinx VivadoRISC-V ISA
ML · Edge AI

NanoSight

TinyML object detection pipeline optimised for microcontrollers. Quantised MobileNet architecture achieving real-time inference on STM32 at under 100mW. Deployed in precision agriculture scenarios.

TensorFlow LiteSTM32Python

Let's Build
Something Together

Whether you want to collaborate on a hardware project, talk startups, or just geek out about RISC-V — my inbox is always open.

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