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Accomplishments

India Semiconductor Workforce Development Program Fellowship – Grade I Awardee

Individual Jun 2025 - Sept 2025
  • Selected as Grade I Fellow from 2,800+ applicants.
  • Completed a 3-month multi-level program covering semiconductor physics, TCAD, and fabrication processes.
  • Modeled semiconductor devices using Synopsys Sentaurus TCAD with geometry and doping parameterization.
  • Automated simulations using Sentaurus Workbench and command-based scripting.
  • Analyzed electrical characteristics, carrier behavior, and field profiles using Sentaurus Inspect and Visual.
  • Recognized among Top Performers of Cohort 5 Custom Module on Electrical Characterization of Material and Device by Tektronix.

Certified Top Performer (Analog Track) | Texas Instruments – TI BYTE Program

May 2025 - July 2025
  • Analog electronics fundamentals, including circuit analysis, & hands-on simulation exercises

FIR Accelerator SoC Proposal Accepted – Microwatt Momentum (OpenPOWER Hardware Design Hackathon)

Individual Proposal 2025
  • Proposal for a parameterizable FIR accelerator SoC accepted at the Microwatt Momentum international hardware design hackathon.
  • Designed a low-power DSP accelerator with configurable taps and coefficients using a Wishbone-Lite interface.
  • Shortlisted for potential fabrication consideration via ChipFoundry and Efabless OpenMPW Shuttle.

ISRO IROC-U Robotics Challenge – Elimination Round Qualifier

Team Dec 2024 Top 24 Nationally
  • Ranked Top 24 nationally in IRoC-U 2025.
  • Built an autonomous drone system for GNSS-denied navigation and surface mapping.
  • Participated in national-level elimination rounds.

FPGA-Based Maze Solver, e-Yantra Robotics Competition (IITB eYRC'26)

Team Oct 2025 - Feb 2026 All India Rank 13
  • Built and verified a 32-bit RISC-V single-cycle core (32x32 regfile, full load/store ISA) in Verilog on Cyclone-IV FPGA (DE0 Nano); Fmax 112 MHz.
  • Integrated ultrasonic and IR sensors for wall detection and obstacle avoidance, with temperature, humidity, and soil moisture sensing for warehouse microclimate data acquisition.
  • Implemented motor control, UART telemetry, and wireless communication to Central Control Unit running in parallel on the custom CPU.
  • Validated full system through sensor calibration routines and live maze runs.

RV32I CPU Design, HackS'US-V Vegathon (RSET IEDC & C-DAC Thiruvananthapuram)

Team Mar 2026 1st Place, National | 42hr Hackathon
  • Designed and compared four RV32I core variants: single-cycle, multi-cycle, 5-stage pipeline (IF-ID-EX-MEM-WB), and dual-issue in-order superscalar.
  • Superscalar core: two-wide fetch, parallel pipelines, inter-lane RAW/WAW hazard detection, load-use stalls, branch squash, 4R2W regfile, and multi-port memory.
  • Evaluated CPI, IPC, and latency across variants using performance counters.
  • Synthesized and demonstrated on Spartan-7 FPGA with LUT utilization comparison across microarchitectures.

Global Placement Optimization, Macro Placement Challenge '26 (Partcl and HRT, IBM Benchmark Suite ICCAD04)

Team 2026 WR Rank 50 | Proxy: 1.3241 | Overlaps: 0
  • Benchmarked on the IBM Benchmark Suite (ICCAD04), where each case includes hard macros to be placed, soft macros that can also be placed, nets connecting all components, and a hand-crafted initial placement used as reference.
  • Achieved a proxy score of 1.3241 with zero macro overlaps, ranked 50 on the world ranking leaderboard.
  • Outperformed RePlAce (proxy 1.4578) and simulated annealing baseline (proxy 2.1251).
  • Built a multi-phase placement pipeline: legalization from initial placement, followed by a WL thermostat using simulated annealing on a sparse pair graph with overlap-safe moves.
  • Applied coarse-to-fine coordinate descent with exact proxy evaluation for refinement.
  • Implemented Large Neighborhood Search (LNS): destroy and repair of connected-macro patches, accepting moves only on proxy improvement.
  • Ran a final soft-macro force-directed pass on the best hard placement found.
  • Used multi-start optimization (3 restarts) with Gaussian perturbation, keeping the global best result across runs.