Monthly summary for 2026-07: Key features delivered: - Dual-fetch frontend pipeline (2-fetch) implementation and optimization for OpenXiangShan/XiangShan. Enabled two cache lines to be fetched in parallel, increasing instruction throughput. Updated FTQ, ICache, and IFU to support the dual-fetch path and applied timing optimizations in related stages. Commits: d97789de12ea371909ebc1bf89a4fbaf136bb994; 2b5769e8b2e3bb0fe63dc8294b9f5db70988567d. - FTQ rolling performance counters: Introduced rolling counters in the Fetch Target Queue to track misprediction types and IFU redirects over a rolling window, improving performance debugging and traceability. Commit: 0e5469ce17236e21d61e60795c3dd2dbf381067e. - Frontend pipeline endPosition refactor: Replaced takenCfiOffset with endPosition across frontend modules to simplify data flow and improve timing in the BPU-to-FTQ path, reducing unnecessary computations. Commit: 0454478c247c7137301276b85173ed5a99f786bf. - XSPerfSeqRolling: Batch registration for multi-counter performance metrics in OpenXiangShan/Utility, enabling cycle-based and event-based rolling modes and streamlining instrumentation of hardware designs. Commit: 98a6e2844191e642f01919be58e9a9db1133cf4d. Major bugs fixed: - Timing refinement for 2-fetch logic: Fixed timing issues related to 2-fetch by relocating realTwoFetchValid decision logic to the MainPipe s0 stage and by storing bankSel and isCrossLine in wayLookup, reducing critical-path pressure and preventing handshake path hazards. This refactor complements the 2-fetch feature and aligns signal routing to improve reliability. Commit: 2b5769e8b2e3bb0fe63dc8294b9f5db70988567d. Overall impact and accomplishments: - Substantial improvements to frontend throughput and observability, enabling more predictable performance and easier debugging across the instruction fetch and execution path. The 2-fetch enhancement increases instruction throughput, the FTQ rolling counters provide actionable insights into mispredictions and redirects, and the endPosition refactor simplifies dataflow and timing in the BPU-FTQ interface. - Strengthened instrumentation and performance analysis capabilities through XSPerfSeqRolling, allowing hardware designers to register and monitor multiple performance counters with a shared prefix in a scalable manner. Technologies/skills demonstrated: - RTL and microarchitectural optimization (2-fetch pipeline, FTQ and ICache timing, endPosition modeling) - Performance instrumentation and diagnostic tooling (rolling counters, XSPerfSeqRolling) - Refactoring for maintainability and timing reliability (endPosition, signal routing adjustments) - Hardware design collaboration and incremental feature integration (commit-based changes across XiangShan and Utility repos)

June 2026: Stabilized training data integrity in OpenXiangShan/XiangShan by fixing a Fetch Target Queue race condition that could cache training data during backend redirects. Implemented a conditional guard to prevent writing to trainCache when a redirect is active, ensuring the BPU receives accurate training signals and reducing the risk of stale data affecting training.

May 2026 performance and reliability enhancements for OpenXiangShan/XiangShan. Key features delivered: Tage Predictor Performance Optimization (dropResolve when no mispredictions to reduce updates, power usage, and read-port conflicts in TAGE/SC); Training pipeline restructuring by splitting the second training stage (t2) into two pipeline stages to improve timing, efficiency, and management of branch information. Major bugs fixed: timing-related issues in the training path addressed by the same pipeline changes (fix tage train timing). Overall impact: increased predictor efficiency and throughput, lower energy per instruction, and easier timing closure with reduced mispredictions and update overhead. Technologies/skills demonstrated: hardware design optimization, TAGE predictor engineering, pipeline design and timing analysis, power-aware optimization, and disciplined commit messaging.

April 2026 monthly summary for OpenXiangShan/XiangShan focused on performance optimizations for the branch predictor and a correctness fix in EnumUInt. The changes improve throughput, accuracy, and validation reliability, contributing to higher hardware efficiency and robustness in the XiangShan pipeline.

March 2026 (2026-03) monthly summary for OpenXiangShan/XiangShan focusing on reliability and performance improvements in the TAGE branch predictor. Delivered a critical bug fix to the TAGE predictor allocation logic that enables correct replacement of entries and reduces the risk of mispredictions. Implemented validity and usefulness masks on predictor entries to prevent incorrect eviction and to improve overall prediction accuracy. The changes align with the bug fix efforts for the TAGE select allocate logic, as captured in the referenced commit and issue (#5677).

February 2026 — OpenXiangShan/XiangShan: Branch Prediction Unit (BPU) improvements focused on accuracy and throughput. Restored reliable taken-mask selection logic for s1 and optimized timing and data flow across pipeline stages, delivering lower misprediction penalties and better speculative throughput for branch-heavy workloads. The work is anchored by targeted fixes in two commits, and early benchmarks indicate a modest performance uplift (approximately 0.09 in SPECint with a 0.3 baseline). This enhances overall processor efficiency and supports higher IPC in real workloads.

January 2026 monthly summary for OpenXiangShan/XiangShan focusing on delivering architectural improvements to the Branch Prediction Unit (BPU) and stabilizing the Tage Predictor training path. The work emphasizes business value through higher prediction accuracy, lower runtime metadata footprint, and improved maintainability to support future feature work.

December 2025 monthly summary for OpenXiangShan/XiangShan focusing on tangible business value and technical execution across the TAGE predictor, BTB/BPU improvements, and branch monitoring. The team delivered core performance and accuracy enhancements, improved debugging capabilities, and richer metrics instrumentation, resulting in more accurate simulations, faster feedback loops, and clearer visibility into branch path behavior.

Month 2025-11 summary for OpenXiangShan/XiangShan: Delivered critical correctness fixes to the branch predictor and major enhancements to the TAGE predictor, with observability improvements via ChiselDB. The changes improve prediction accuracy, reduce mis-holds, and enhance pipeline stability while enabling faster debugging and performance tuning through tracing. Key work spanned fixes to ABTB hold behavior and CTR initialization, performance and allocation logic improvements, and ChiselDB integration for end-to-end visibility.

OpenXiangShan/XiangShan — October 2025 monthly highlights. Focused on improving Branch Predictor reliability and the data-path, delivering correctness fixes and performance enhancements that directly impact throughput and accuracy. Key changes include correctness fixes to Tage predictor providerIdxOH and MainBtb hitMask position logic, which reduce misprediction rates. In addition, Branch Predictor performance and data-path enhancements were implemented: new BP performance counters, DecoupledIO-based backpressure for the resolveQueue, and FastTrain IO for ABTB training to accelerate model updates. Ftq/BaseTable refinements were also completed: Ftq write requests refactored to use a Queue and Tage base table next-set index logic was corrected. Impact: higher branch prediction accuracy, improved training throughput, and a more robust, scalable data-path. Technologies/skills demonstrated include backpressure design (DecoupledIO), performance instrumentation, FastTrain IO, queue-based data-path, and targeted code refactoring for reliability.

September 2025 – OpenXiangShan/XiangShan monthly summary: Focused on performance-oriented enhancements to the BPU, fetch, and alignment infrastructure. Delivered TAGE-based BPU enhancements with training/prediction separation and improved misprediction handling, connected TAGE with MainBTB, and simplified output to condTakenMask. Implemented first-mispredict branch-driven training for MBTB and TAGE to accelerate adaptation. Expanded BTB/Alignment Bank capacity with 8-way ABTB and support for NumAlignBanks > 2. Introduced a Tag Table write buffer Queue to enable concurrent writes and boost throughput. Improved IFU flush reliability with corrected s1 flush condition. These changes improved branch prediction accuracy, reduced stall risk, and increased overall instruction throughput, delivering measurable business value in performance, energy efficiency, and scalability.

August 2025 focused on enhancing the clarity and maintainability of the MGSC (Multi-Grain State Correlation) branch predictor in GEM5 for the OpenXiangShan project. The primary delivery was a parameter renaming effort to use descriptive names for table numbers, history lengths, and index widths, aligning with existing conventions and reducing ambiguity for future tuning and reviews. This work strengthens code readability and supports safer performance modeling as part of ongoing predictor improvements.

OpenXiangShan/XiangShan — April 2025: Focused on frontend memory subsystem optimization to reduce bottlenecks and improve throughput in Ftq. Delivered a targeted SRAM configuration tuning and a code-path fix that improves data handling and eliminates mis-identification of memory paths. These changes are backed by two commits for traceability and faster future tuning. Overall impact: improved frontend data path throughput, reduced risk of timing/path misclassification, and smoother Ftq SRAM operation, enabling more robust hardware builds and upcoming performance features.

Concise monthly summary for 2025-03 focused on delivering architecturally meaningful features, stabilizing core subsystems, and enabling future growth. Highlights include a Rocket-chip submodule upgrade with a fix to instruction decoding for c.addi when destination is x0, and a SRAM-centric refactor introducing a SplittedSRAM module to support scalable meta SRAM configurations.

February 2025: Fixed Jalr Prediction Taken logic in PreDecode for OpenXiangShan/XiangShan to improve branch prediction accuracy and overall reliability. Patch ensures correct detection of taken predictions for jalr, addressing reliability gaps flagged in #4269. Commit 7f475a241b2cdf869833f641138fdf66b32c9bd6.

January 2025 – OpenXiangShan/XiangShan: Focused improvements on the Instruction Fetch Unit (IFU) to strengthen correctness and control-flow reliability while reducing unnecessary flush activity. Delivered two targeted changes: (1) IFU flush optimization removing redundant BPU override flush logic to simplify flush signal generation and improve control flow, and (2) IFU misprediction handling for jalr by adding range checks to terminate instruction blocks on misprediction. These changes improve fetch-path correctness, reduce risk of executing incorrect instructions after mispredictions, and lower maintenance burden. Business value: more predictable performance, fewer corner-case bugs, and a cleaner, more maintainable IFU code path.

Concise monthly summary for 2024-11 highlighting delivered features, fixed issues, impact, and technologies demonstrated for OpenXiangShan/XiangShan. Focus on business value and technical achievements, with clear references to delivered commits.

Month 2024-10 summary focusing on expanding RISC-V Zcb ISA coverage and improving correctness across the XiangShan/OpenXiangShan stack. Delivered a targeted bug fix for illegal instruction checks related to zcb arithmetic in the Rocket-chip subproject, and added RISC-V Zcb extension support in NEMU by enabling the extension, introducing new Zcb arithmetic definitions, and updating the decoder and execution helpers. These changes enhance ISA compliance, reduce misinterpretation of Zcb instructions, and enable end-to-end emulation and testing of Zcb operations.