Parika Gupta

Reseach and Development Engineer

Synopsys

India

01.2020 - Current

RTL Chain Structure Optimization (May 2025 – Present):
Led initiatives to optimize Register Transfer Level (RTL) chain structures, utilizing advanced timing analysis and design techniques. Achieved improvements in timing closure, reduction of silicon area, and enhanced overall design reliability, contributing to higher efficiency in large-scale digital systems.
Debugging Framework Development (July 2024 – April 2025): Developed a robust debugging framework, enabling dynamic control of features via command inputs and configurable debug levels. Enhanced troubleshooting efficiency by allowing granular feature toggling and real-time diagnostics, supporting more effective development and maintenance workflows.
TCL to C++ Automation Script Migration (June 2023 – June 2024): Modernized customer automation workflows by refactoring TCL scripts into C++. Applied STL and advanced C++ paradigms to efficiently replicate complex control flows and data structures, resulting in increased execution speed, maintainability, and seamless integration within distributed environments.
Flow Consolidation for Enhanced Restriction Support (Jan 2023 - May 2023): Consolidated flow logic to improve restriction support, streamlining multiple code paths into unified processes. Enhanced system flexibility and maintainability by simplifying the enforcement of complex restrictions, resulting in more robust and scalable solutions.
Critical Quality Improvements in Legacy Toolchains (Nov 2022 - May 2023): Identified and resolved 8+ crash and runtime issues within mature, production-critical features (infer_mux_override, muxTiming), leading to a 67% reduction in customer-reported bugs. Strengthened toolchain reliability and robustness, directly improving user experience, and operational stability in high-scale environments.
Boost Graph Library (BGL) Efficiency Improvement (Jan 2021 – Dec 2021): Enhanced graph runtime performance by reducing overhead from 10.8% to 0.1% through the elimination of redundant graph build operations. Leveraged deep knowledge of BGL, adjacency lists, and property maps to implement optimized traversal and reuse strategies, leading to reliable, high-performance solutions for large-scale applications.
Congestion Trajectory Analysis Automation (Jan 2020 – Dec 2020): Developed a Python-based framework to analyze congestion trajectories after each flow step, enabling early detection of potential routing bottlenecks. Automated data collection and visualization, highlighting chances for flow optimization, and enhancing design quality through actionable insights.