OpenXiangShan/XiangShan — August 2025 monthly summary: Focused on stabilizing the IFU enqueue logic to prevent stalls when the IBuffer is full. Delivered a targeted bug fix for non-cacheable (nc) instructions passing through the MMIO channel, ensuring IBuffer fullness is checked before enqueuing. This reduces stalls and errors, improving reliability of speculative execution and the MMIO path under high-load scenarios. The work enhances pipeline predictability and supports safer operation in production workloads.

July 2025 monthly summary for OpenXiangShan/XiangShan: Delivered a critical MMIO correctness fix to prevent premature bus requests during speculative MMIO fetch, improving pipeline stability and memory-mapped IO reliability.

The March 2025 cycle delivered notable enhancements to the branch predictor and memory subsystem across XiangShan and Utility, with a focus on fault tolerance, configurability, and memory scalability. Dynamic disabling of the return-address stack (RAS) on imminent overflow reduces stalls, while refactoring ITTage/Tage SRAM configurations enables finer-grained usage for potential gains in predictive accuracy and throughput. ICache SRAM was split to align with backend memory organization, improving scalability. In Utility, SplittedSRAMTemplate support was added and FoldedSRAMTemplate integration was completed; subsequent fixes corrected split-parameter handling and addressed X-propagation, ensuring robust SRAM configuration and signal flow. Overall, these changes improve performance, reliability, and maintainability of the memory and branch-predictor subsystems, delivering clear business value through higher throughput and better hardware utilization.

February 2025 performance month: delivered targeted enhancements and robustness improvements across OpenXiangShan/XiangShan and OpenXiangShan/NEMU. Focused on business value by improving timing performance, reliability, and build-time correctness through a controlled clock gating optimization and a decoder robustness fix.

January 2025 (2025-01) OpenXiangShan/XiangShan monthly summary. This period focused on performance improvements for jump address prediction and reliability of RAS redirection. Key features delivered include ITTAGE timing optimization with loop-bound adjustments and a mask-target approach to ensure precise address masking, along with region-aware addressing refinements. Major bug fix addressed RAS redirection accuracy when encountering invalid instructions to prevent false predictions. Overall impact: increased predictability and throughput of control-flow decisions, reduced stalls due to mispredictions, and improved robustness in the presence of invalid instruction sequences. Technologies/skills demonstrated: microarchitectural optimization, ITTAGE-based prediction tuning, mask-based address calculations, region-aware addressing, and careful commit-level change management.

December 2024 monthly summary: Across XS-MLVP/UnityChipForXiangShan and OpenXiangShan/XiangShan, delivered verification refinements, frontend robustness, and reset-state improvements. Key outcomes include a false-positive filter for illegal RVC instructions that tightens validation and reduces noise in test results; fortified instruction fetch/branch handling with FtqPtr-based instruction pointer management and improved error checking to increase pipeline reliability; and a fix to BOS/return stack pointer update during reset to improve fault tolerance. These developments enhance test quality, shorten verification cycles, and improve hardware reliability. Technologies/skills demonstrated include RISC-V decoding validation, FtqPtr-based pointer management, RegEnable semantics, code refactoring for configuration parameters, and cross-repo collaboration.

Monthly summary for 2024-11: Delivered targeted frontend documentation enhancements and RISC-V instruction decoding verification improvements across two repositories. Improvements improved navigation, documentation accuracy, and verification coverage, accelerating developer onboarding and reducing risk in instruction handling. Key outcomes include direct frontend code links in docs, corrected reference links, and new RVI decoding checkpoints with illegal/complex instruction detection, together boosting overall quality and maintainability.