HPC RISC-V CPU @Tenstorrent | IIT Madras'23
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Assessing your cultural and operational fit
Tenstorrent
Data Scientist
June 13, 2026 – Present
Accelerating-Mandelbrot-Fractal-on-FPGA
April 13, 2022 – November 19, 2022
Hardware acceleration of mandelbrot fractal generation on PYNQ-Z1 FPGA as part of EE5332 IIT Madras course project
View ProjectAccelerating_Standard_and_Modified_AES128
November 23, 2021 – December 10, 2021
Accelerating the AES algorithm on an FPGA and comparing the speedup with both AES and Modified AES algorithms
View ProjectPSRV32-Code-Base
August 2, 2021 – January 2, 2022
The Private Code base that has been used for PSRV32 Processor.
View ProjectEE2016-Microprocessor-Lab-IITM
July 13, 2021 – July 27, 2021
Contains the course work done as a part of Microprocessor theory &lab course at IITM during the fall 2020.
View ProjectEE2703-Applied-Programming-Lab-IITM
July 13, 2021 – July 13, 2021
Contains the code and reports of the assignments done as a part of EE2703 APL Course at IITM during the Spring 2021.
View ProjectCustom-Dev-Board
January 24, 2021 – July 18, 2021
Getting started with the ESP32 based Custom Dev Board
View ProjectElectronics-Club
April 21, 2020 – June 28, 2020
This repo contains the work and tasks done in Elec-Club
View ProjectCultural Fit Analysis
The candidate's projects show a strong inclination towards hardware design, embedded systems, and low-level programming (Verilog, Assembly, C++). While there's a stated target role of 'Data Scientist' and some Python/Jupyter experience, the majority of projects do not directly align with typical data science responsibilities (e.g., statistical modeling, machine learning, data analysis, big data technologies). This suggests a potential mismatch in core interests and project experience for a pure Data Scientist role, though the hardware acceleration background could be valuable in specialized data science roles focusing on performance optimization or edge AI.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The provided data primarily focuses on technical projects and lacks information on collaboration, problem-solving approaches, or communication styles.