February 2026 OpenXiangShan/XiangShan: No new features released this month; focused on targeted bug fixes to improve timing accuracy and memory operation correctness, with clear commit-driven changes across critical subsystems.

OpenXiangShan/XiangShan — January 2026 monthly summary Key features delivered and business value: - Backend V2 Configuration and Scheduling Enhancements: Added BackendV2Config and new scheduling parameters (scheduler, rob, regfile) with regCacheEn flag to tune performance and configurability, enabling broader scheduling capabilities and improved backend throughput. - VTypeBuffer Commit Width Upgrade: Replaced CommitWidth with RabCommitWidth across VTypeBuffer and related components, improving compatibility with the new commit width and overall performance. - IssueQueue Core Improvements and Bug Fixes: Fixed ALU source data for jump UOPs, corrected wakeup logic to prevent regCache cancellation, aligned wakeup destination width with backend parameters, and introduced Og1Payload timing improvements to stabilize entries selection timing. - IssueQueue Code Cleanup and Refactor: Eliminated unused bundles/classes, streamlined definitions, and modularized data path and bypass network; adopted Og0InUop and Og1InUop in data path. Major bugs fixed: - ALU source data correctness for jalr/jal uops. - Wakeup cancellation issues in cross-region wakeups and regCache handling. - Wakeup destination width misalignment. - Timing path refinements: Og1Payload integration and flushCopyReg timing fix. Overall impact and accomplishments: - Achieved configurable, scalable backend scheduling with measurable performance tuning via regCacheEn. - Improved correctness, timing stability, and throughput in IssueQueue pipelines. - Reduced technical debt and improved maintainability through targeted code cleanup and refactors. Technologies/skills demonstrated: - Hardware/RTL pattern improvements, scheduling/configuration design, and performance tuning. - Rigorous commit hygiene and incremental refactoring with clear traceability. - Cross-module coordination to align backend params, VI/OG pipelines, and timing.

Monthly performance summary for OpenXiangShan/XiangShan - December 2025. This period focused on delivering key architectural enhancements to the issue queue and register file, along with targeted performance optimizations and maintainability improvements. The work improves concurrency, timing predictability, and data-path efficiency, while fixing correctness issues that affected reads and flush behavior. The combined effort strengthens core throughput and system stability for real workloads.

November 2025 performance summary highlighting reliability, timing, and correctness improvements across two OpenXiangShan repos. The month focused on stabilizing FP operations, improving instruction handling paths, and streamlining wakeup and flush logic for better data integrity and cycle timing.

In October 2025, XiangShan delivered a set of backend and architectural enhancements that increase floating-point throughput, strengthen observability, and improve maintainability. The work centers on expanding the FP execution path, enabling more flexible ALU configurations, and tightening backend reliability with monitoring and micro-op handling. Collectively, these changes establish a stronger foundation for performance tuning and future optimizations, translating to higher throughput, more predictable latency, and easier long-term maintenance across workloads.

2025-09 highlights for OpenXiangShan/XiangShan: delivered architectural and data-path improvements aimed at reliability, maintainability, and performance; fixed critical frontend correctness issues; enabled vector processing scaffolding for future performance. Key achievements below with traceable commits. Overall impact: the month delivered concrete stability and maintainability improvements across the frontend and backend, improved FP/Int data-path integration, and laid groundwork for vector-enabled performance, driving future efficiency and ease of maintenance. Business value includes reduced risk of mispredictions, clearer modularization for partitioning logic, improved data-path reliability, and a cleaner path toward vector workloads.

August 2025 (OpenXiangShan/XiangShan) focused on enhancing instruction flow accuracy, improving branching support, and stabilizing the backend/frontend integration around ftqOffset. The team delivered key features to improve PC management and frontend-backend coordination, while addressing stability concerns introduced by the new ftqOffset with a controlled ROB configuration.

During January 2025, delivered critical fixes and robustness improvements across OpenXiangShan/YunSuan and OpenXiangShan/XiangShan. The work prioritized compiler correctness, reliability, and maintainability, ensuring solid foundations for upcoming features and performance-sensitive workflows. Key engineering efforts included targeted bug fixes that unblock builds and improve correctness, along with strategic dependency upgrades to stabilize upstream integrations while preserving functional behavior.

December 2024 monthly summary for OpenXiangShan/XiangShan focusing on backend and EXU area pipelining optimizations, timing improvements, and a broad set of bug fixes that improved throughput, timing determinism, and reliability. Delivered a coordinated set of features and fixes with precise commit-level traceability across the core pipeline, including register file reconfiguration to enable higher parallelism and more robust dispatch/FP paths.

November 2024: OpenXiangShan/XiangShan backend modernization and VfScheduler optimization. Key changes include a backend overhaul to improve dispatch, memory handling, and pipeline efficiency; consolidation of memory pools; and targeted resource reductions to shrink area. Iterative tuning of VfScheduler/VFEX3 yielded throughput gains, followed by a rollback after validation to ensure correctness. Overall, these changes delivered higher throughput, lower latency, and a smaller hardware footprint, with strong emphasis on performance-per-watt and scalability.