
Shruthi Gopalakrishna developed advanced compiler tooling and cloud deployment workflows in the google/heir repository, focusing on secure computation and scalable infrastructure. She engineered features such as batched LUT3 operation conversion, boolean vectorization, and polynomial evaluation passes, leveraging C++, Python, and MLIR to optimize intermediate representation and pipeline performance. Her work included robust build system enhancements using Bazel, cloud provisioning automation for Google Cloud TPUs, and disciplined deprecation of legacy GCP scripts to streamline maintenance. By addressing memory model correctness, data packing reliability, and documentation clarity, Shruthi demonstrated depth in low-level optimization, DevOps, and type-driven code generation across complex workflows.

October 2025 monthly summary for google/heir. Focused on delivering enhanced polynomial handling in the heir-polynomial-to-llvm conversion pipeline. Key work included introducing a new pass createLowerPolynomialEval in the polynomialToLLVMPipelineBuilder to enable evaluation of polynomial expressions during LLVM IR generation, along with a dedicated test lower_poly_eval to verify correctness within the conversion pipeline. The feature is exposed via a configurable flag (--lower-polynomial-eval) to control its activation in the pipeline. Committed work includes 98d3d340ede863d3d1dd92548d304b639436abbe with the message: Add a test for lower poly eval. Also add --lower-polynomial-eval pass to heir-polynomial-to-llvm pipeline.
October 2025 monthly summary for google/heir. Focused on delivering enhanced polynomial handling in the heir-polynomial-to-llvm conversion pipeline. Key work included introducing a new pass createLowerPolynomialEval in the polynomialToLLVMPipelineBuilder to enable evaluation of polynomial expressions during LLVM IR generation, along with a dedicated test lower_poly_eval to verify correctness within the conversion pipeline. The feature is exposed via a configurable flag (--lower-polynomial-eval) to control its activation in the pipeline. Committed work includes 98d3d340ede863d3d1dd92548d304b639436abbe with the message: Add a test for lower poly eval. Also add --lower-polynomial-eval pass to heir-polynomial-to-llvm pipeline.
September 2025 monthly summary for google/heir repository focusing on client interface improvements and data packing reliability.
September 2025 monthly summary for google/heir repository focusing on client interface improvements and data packing reliability.
August 2025 (Month: 2025-08) -- google/heir: Key features delivered: - Removed GCP TPU provisioning scripts and deprecated GCP integration to decouple the repository from direct Google Cloud provisioning. - Deleted documentation and Python scripts for provisioning, running, and destroying TPU resources on GCP, reducing cloud-provider surface area and maintenance overhead. Major bugs fixed: - No major bugs reported for google/heir in this period based on available data; effort focused on deprecation and cleanup rather than bug fixes. Overall impact and accomplishments: - Simplified maintenance and improved security/compliance posture by decoupling from GCP, aligning with broader platform strategy. - Reduced operational overhead and risk associated with TPU provisioning tooling, enabling easier migration to alternative infrastructures in the future. - Demonstrated disciplined deprecation strategy and clean-up of cloud-specific tooling, paving the way for more modular resource management. Technologies/skills demonstrated: - Python scripting cleanup and documentation removal, with careful version control discipline. - Cross-repo tooling awareness, evidenced by related activity in google/fully-homomorphic-encryption (commit bfcb519aca28f186d3d8739f3f1ffc4d2865edcc). - Focus on business value through simplification, governance, and maintainability of cloud integrations.
August 2025 (Month: 2025-08) -- google/heir: Key features delivered: - Removed GCP TPU provisioning scripts and deprecated GCP integration to decouple the repository from direct Google Cloud provisioning. - Deleted documentation and Python scripts for provisioning, running, and destroying TPU resources on GCP, reducing cloud-provider surface area and maintenance overhead. Major bugs fixed: - No major bugs reported for google/heir in this period based on available data; effort focused on deprecation and cleanup rather than bug fixes. Overall impact and accomplishments: - Simplified maintenance and improved security/compliance posture by decoupling from GCP, aligning with broader platform strategy. - Reduced operational overhead and risk associated with TPU provisioning tooling, enabling easier migration to alternative infrastructures in the future. - Demonstrated disciplined deprecation strategy and clean-up of cloud-specific tooling, paving the way for more modular resource management. Technologies/skills demonstrated: - Python scripting cleanup and documentation removal, with careful version control discipline. - Cross-repo tooling awareness, evidenced by related activity in google/fully-homomorphic-encryption (commit bfcb519aca28f186d3d8739f3f1ffc4d2865edcc). - Focus on business value through simplification, governance, and maintainability of cloud integrations.
July 2025 monthly summary for google/heir: Focused delivery on GCP usage enhancements, key management improvements, and build/docs reliability. Deliverables emphasize business value through easier deploys, better security, and clearer guidance for developers.
July 2025 monthly summary for google/heir: Focused delivery on GCP usage enhancements, key management improvements, and build/docs reliability. Deliverables emphasize business value through easier deploys, better security, and clearer guidance for developers.
June 2025 monthly summary for google/heir: Delivered enhancements to GCP provisioning scripts to improve compatibility and performance. Key changes include adding necessary package installations and adjusting default configurations to streamline GCP resource setup. Result: faster, more reliable GCP deployments with reduced manual steps and lower configuration drift.
June 2025 monthly summary for google/heir: Delivered enhancements to GCP provisioning scripts to improve compatibility and performance. Key changes include adding necessary package installations and adjusting default configurations to streamline GCP resource setup. Result: faster, more reliable GCP deployments with reduced manual steps and lower configuration drift.
April 2025 monthly summary for google/heir: Focused on stabilizing the build and improving deployment scripts. Key outcomes include a Yosys Build Script Location Fix within Bazel integration, and substantive GCP script enhancements for Heir. These work items reduced build failures, clarified paths, and improved code quality and maintainability across CI and developer workflows.
April 2025 monthly summary for google/heir: Focused on stabilizing the build and improving deployment scripts. Key outcomes include a Yosys Build Script Location Fix within Bazel integration, and substantive GCP script enhancements for Heir. These work items reduced build failures, clarified paths, and improved code quality and maintainability across CI and developer workflows.
March 2025 — google/heir: Focused on building scalable, configurable OpenFHE workflows and reducing build friction. No major bugs fixed this month. Key deliverables include: configurable passing of heir-opt flags (with parallelism options) in the heir-jaxite build flow; a new Bazel macro to streamline OpenFHE library creation from MLIR via heir-opt and heir-translate; and a refactor of end-to-end tests to use the new macro. These changes accelerate MLIR-based library production, improve reproducibility, and enable more performant builds.
March 2025 — google/heir: Focused on building scalable, configurable OpenFHE workflows and reducing build friction. No major bugs fixed this month. Key deliverables include: configurable passing of heir-opt flags (with parallelism options) in the heir-jaxite build flow; a new Bazel macro to streamline OpenFHE library creation from MLIR via heir-opt and heir-translate; and a refactor of end-to-end tests to use the new macro. These changes accelerate MLIR-based library production, improve reproducibility, and enable more performant builds.
February 2025 - google/heir: Implemented a MemRef flattening fix in JaxiteEmitter and introduced a flattenedIndex utility to support multi-dimensional indexing in flattened arrays. Delivered a robust end-to-end test for a fully connected layer to validate integration and prevent regressions. Commit: cffd7a6a8c9362ff17299ce280dfb3903ae9f479. Overall impact: improved correctness and reliability of memory access paths in the JaxiteEmitter backend, reducing runtime errors when handling flattened MemRef data and laying groundwork for future performance optimizations. Technologies/skills: backend emitter development, memory-model awareness, test-driven development, and efficient multi-dimensional indexing in flattened representations.
February 2025 - google/heir: Implemented a MemRef flattening fix in JaxiteEmitter and introduced a flattenedIndex utility to support multi-dimensional indexing in flattened arrays. Delivered a robust end-to-end test for a fully connected layer to validate integration and prevent regressions. Commit: cffd7a6a8c9362ff17299ce280dfb3903ae9f479. Overall impact: improved correctness and reliability of memory access paths in the JaxiteEmitter backend, reducing runtime errors when handling flattened MemRef data and laying groundwork for future performance optimizations. Technologies/skills: backend emitter development, memory-model awareness, test-driven development, and efficient multi-dimensional indexing in flattened representations.
January 2025 monthly summary focused on enabling cloud-scale FHE deployment and testing on Google Cloud TPUs using HEIR and Jaxite. Delivered an end-to-end cloud workflow, provisioning scripts, and demonstration program to validate FHE deployments on TPUs. This work lays the foundation for scalable experimentation and benchmarking of FHE workloads in production-like cloud environments.
January 2025 monthly summary focused on enabling cloud-scale FHE deployment and testing on Google Cloud TPUs using HEIR and Jaxite. Delivered an end-to-end cloud workflow, provisioning scripts, and demonstration program to validate FHE deployments on TPUs. This work lays the foundation for scalable experimentation and benchmarking of FHE workloads in production-like cloud environments.
December 2024: Delivered a Boolean Vectorizer in the TOSA-to-Jaxite pipeline for google/heir, introducing a boolean vectorization pass and a new parallelism option. The vectorizer is activated when parallelism > 0 and is integrated into the pipeline flow, with a dedicated test example for pmap_add_one_lut3 to validate functionality. This work enhances data-parallel performance in the translation path and expands TOSA->Jaxite capabilities. Commit 733c915347e16707c67617cd31bb12474788c4d9 documents the change and serves as the integration point for this feature.
December 2024: Delivered a Boolean Vectorizer in the TOSA-to-Jaxite pipeline for google/heir, introducing a boolean vectorization pass and a new parallelism option. The vectorizer is activated when parallelism > 0 and is integrated into the pipeline flow, with a dedicated test example for pmap_add_one_lut3 to validate functionality. This work enhances data-parallel performance in the translation path and expands TOSA->Jaxite capabilities. Commit 733c915347e16707c67617cd31bb12474788c4d9 documents the change and serves as the integration point for this feature.
Delivered Packed Lut3 Batched Processing for CGGI to Jaxite conversion in google/heir, introducing PackedLut3Op conversion and emitter logic to batch-process Lut3 ops. This enables higher throughput for batched Lut3 operations and reduces per-op overhead in the CGGI→Jaxite conversion pass, supporting scalable model conversions and improved runtime performance.
Delivered Packed Lut3 Batched Processing for CGGI to Jaxite conversion in google/heir, introducing PackedLut3Op conversion and emitter logic to batch-process Lut3 ops. This enables higher throughput for batched Lut3 operations and reduces per-op overhead in the CGGI→Jaxite conversion pass, supporting scalable model conversions and improved runtime performance.
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