Patrick McEwen developed advanced hardware/software co-design infrastructure in the tathagatasrimani/codesign repository, focusing on scalable modeling, optimization, and benchmarking for modern architectures. He engineered robust flows for BSIM4 and Virtual Source transistor models, integrated CVXPY-based optimization, and enhanced scheduling and register allocation to support multi-memory systems. Using Python and C++, Patrick refactored core modules for maintainability, improved simulation fidelity, and streamlined benchmarking for workloads like ResNet and GEMM. His work addressed complex challenges in hardware modeling, performance analysis, and workflow automation, delivering a maintainable, extensible platform that accelerates design-space exploration and supports reproducible, high-throughput engineering and research workflows.
October 2025 performance summary for tathagatasrimani/codesign. Delivered foundational optimization flow and multiple subproblem strategies, including approximate and non-convex subproblems, with progress saving and performance enhancements. Implemented robust scheduling and hardware-modeling improvements (ResNet vector generation, AHMDAL limit, OpenRoad area/scale refinements, new metal layers, and wire length normalization). Significant reliability and stability gains via broad bug fixes across optimization, DSP usage, environment setup, and testing workflows, plus improvements to experiment workflows, logging, and deployment hygiene. Enabled higher throughput with concurrent codesign runs and improved visual feedback via dynamic plotting. Demonstrated strong technical skills in optimization, hardware design tooling, and software maintenance, delivering measurable business value: faster iterations, scalable workflows, and consistent results in test environments.
October 2025 performance summary for tathagatasrimani/codesign. Delivered foundational optimization flow and multiple subproblem strategies, including approximate and non-convex subproblems, with progress saving and performance enhancements. Implemented robust scheduling and hardware-modeling improvements (ResNet vector generation, AHMDAL limit, OpenRoad area/scale refinements, new metal layers, and wire length normalization). Significant reliability and stability gains via broad bug fixes across optimization, DSP usage, environment setup, and testing workflows, plus improvements to experiment workflows, logging, and deployment hygiene. Enabled higher throughput with concurrent codesign runs and improved visual feedback via dynamic plotting. Demonstrated strong technical skills in optimization, hardware design tooling, and software maintenance, delivering measurable business value: faster iterations, scalable workflows, and consistent results in test environments.
September 2025 performance summary for tathagatasrimani/codesign. Focused delivery across end-to-end scheduling, interface enhancements, and benchmarking readiness, elevating throughput, reliability, and business value for hardware/software co-design. Key outcomes include ScaleHLS UI/interface improvements with delay customization, robust schedule parsing (including multi-port memories), and integrated netlist-to-schedule flow for GEMM/Jacobi/ResNet with ResNet benchmarking. Preparations for additional Vitis interfaces and ongoing CNFET/MVS modeling laid groundwork for broader platform support. Enhanced execution-time measurement, debuggability, and a parallelism-driven performance push underpin rapid exploration and target tuning. Overall impact: accelerated design-space exploration, reduced configuration friction, and a scalable, testable path toward production-ready Vitis/ResNet workflows with measurable performance signals.
September 2025 performance summary for tathagatasrimani/codesign. Focused delivery across end-to-end scheduling, interface enhancements, and benchmarking readiness, elevating throughput, reliability, and business value for hardware/software co-design. Key outcomes include ScaleHLS UI/interface improvements with delay customization, robust schedule parsing (including multi-port memories), and integrated netlist-to-schedule flow for GEMM/Jacobi/ResNet with ResNet benchmarking. Preparations for additional Vitis interfaces and ongoing CNFET/MVS modeling laid groundwork for broader platform support. Enhanced execution-time measurement, debuggability, and a parallelism-driven performance push underpin rapid exploration and target tuning. Overall impact: accelerated design-space exploration, reduced configuration friction, and a scalable, testable path toward production-ready Vitis/ResNet workflows with measurable performance signals.
August 2025 monthly summary for tathagatasrimani/codesign. Key initiatives centered on advancing high-fidelity hardware modeling and scalable workflows. Virtual Source (VS) modeling: introduced, integrated, and validated across technology nodes; added configurability (tunable body thickness) and VS-specific equations, with YAML-based configuration and improved validation. BSIM4: enhanced configurability and accuracy, exposing effect toggles and updating parameters for dielectric constants, gate leakage, drain current, and capacitance. Optimization framework: improved workflow and scheduling around hardware modeling; added plotting linked to objective functions, new optimization constraints, and multicycle DFG optimization support. Project structure: refactor for maintainability, reorganized hardware model, forward/inverse passes, and import paths. These efforts increased modeling accuracy, reduced validation risk, and accelerated iteration, delivering business value through better design insights and scalable engineering processes. Technologies demonstrated include CVXPY integration for convex optimization, YAML-driven configuration, and Python-based tooling for model validation and scheduling.
August 2025 monthly summary for tathagatasrimani/codesign. Key initiatives centered on advancing high-fidelity hardware modeling and scalable workflows. Virtual Source (VS) modeling: introduced, integrated, and validated across technology nodes; added configurability (tunable body thickness) and VS-specific equations, with YAML-based configuration and improved validation. BSIM4: enhanced configurability and accuracy, exposing effect toggles and updating parameters for dielectric constants, gate leakage, drain current, and capacitance. Optimization framework: improved workflow and scheduling around hardware modeling; added plotting linked to objective functions, new optimization constraints, and multicycle DFG optimization support. Project structure: refactor for maintainability, reorganized hardware model, forward/inverse passes, and import paths. These efforts increased modeling accuracy, reduced validation risk, and accelerated iteration, delivering business value through better design insights and scalable engineering processes. Technologies demonstrated include CVXPY integration for convex optimization, YAML-driven configuration, and Python-based tooling for model validation and scheduling.
July 2025 monthly summary for tathagatasrimani/codesign. Focus on delivering robust BSIM4 transistor modeling, architecture improvements for tech models, and groundwork for scalable, reliable simulations. Key business value delivered through enhanced model accuracy, performance potential, and maintainability.
July 2025 monthly summary for tathagatasrimani/codesign. Focus on delivering robust BSIM4 transistor modeling, architecture improvements for tech models, and groundwork for scalable, reliable simulations. Key business value delivered through enhanced model accuracy, performance potential, and maintainability.
February 2025 monthly summary for tathagatasrimani/codesign. Focused on delivering core architecture improvements and establishing a robust hardware-design workflow with stronger memory modeling, scheduling accuracy, and graph/IR robustness. Resulted in improved simulation fidelity for multi-memory systems, more reliable scheduling decisions, and scalable infrastructure for hardware exploration.
February 2025 monthly summary for tathagatasrimani/codesign. Focused on delivering core architecture improvements and establishing a robust hardware-design workflow with stronger memory modeling, scheduling accuracy, and graph/IR robustness. Resulted in improved simulation fidelity for multi-memory systems, more reliable scheduling decisions, and scalable infrastructure for hardware exploration.
January 2025 performance summary for tathagatasrimani/codesign focused on strengthening modeling accuracy, debugging efficiency, and scheduling reliability to accelerate design-space exploration and decision-making. Delivered enhancements to the inverse pass and cacti controls, corrected latency scaling behavior, and introduced parasitics into the computation DFG. Strengthened debugging capabilities with a no-cacti flag and critical-path support, while improving documentation, tests, and tooling for maintainability and onboarding.
January 2025 performance summary for tathagatasrimani/codesign focused on strengthening modeling accuracy, debugging efficiency, and scheduling reliability to accelerate design-space exploration and decision-making. Delivered enhancements to the inverse pass and cacti controls, corrected latency scaling behavior, and introduced parasitics into the computation DFG. Strengthened debugging capabilities with a no-cacti flag and critical-path support, while improving documentation, tests, and tooling for maintainability and onboarding.
2024-12 Monthly Summary for tathagatasrimani/codesign focusing on feature delivery, bug fixes, and overall impact.
2024-12 Monthly Summary for tathagatasrimani/codesign focusing on feature delivery, bug fixes, and overall impact.
November 2024 monthly summary for tathagatasrimani/codesign. Delivered critical fixes and feature improvements across energy/latency scaling, symbolic expressions optimization, CACTI tooling, and data provenance for reproducible figures, plus a bug fix for unit scaling. These efforts enhanced model fidelity, plotting reproducibility, performance, and maintainability, enabling exact re-analysis of results and reducing manual debugging effort.
November 2024 monthly summary for tathagatasrimani/codesign. Delivered critical fixes and feature improvements across energy/latency scaling, symbolic expressions optimization, CACTI tooling, and data provenance for reproducible figures, plus a bug fix for unit scaling. These efforts enhanced model fidelity, plotting reproducibility, performance, and maintainability, enabling exact re-analysis of results and reducing manual debugging effort.
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