OpenXiangShan/XiangShan
Aug 2025 – Nov 2025
4 Months active
Languages Used
Scala
Technical Skills
ChiselScalabackend developmentbranch predictionhardware designsystem architecture
Muzi
PROFILE
Muzi
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
20Total
Bugs
1
Commits
20
Features
8
Lines of code
2,341
Activity Months4
Your Network
69 peopleSame Organization
@mails.ucas.ac.cn16
Work History
November 2025
4 Commits • 2 Features
Nov 1, 2025Monthly summary for 2025-11 for OpenXiangShan/XiangShan highlighting key features delivered, major stability work, and business impact. Focused on streamlining the commit datapath, simplifying frontend-backend contracts, and improving branch prediction through a new RAS-centric mechanism. The month included refactors and a new CommitQueue-based approach to train the RAS on commit events, with explicit maintenance of instruction handling semantics.
4 Commits • 2 Features
Nov 1, 2025Monthly summary for 2025-11 for OpenXiangShan/XiangShan highlighting key features delivered, major stability work, and business impact. Focused on streamlining the commit datapath, simplifying frontend-backend contracts, and improving branch prediction through a new RAS-centric mechanism. The month included refactors and a new CommitQueue-based approach to train the RAS on commit events, with explicit maintenance of instruction handling semantics.
November 2025
October 2025
6 Commits • 3 Features
Oct 1, 2025Month 2025-10 highlights three major features/bug fixes delivered to OpenXiangShan/XiangShan, focused on correctness, efficiency, and control-flow reliability. The work reduces re-evaluation, cuts backend dependency needs, and improves timing-aware storage orchestration across the ResolveQueue, FTQ handling, and BPU-control flow paths. Key outcomes: - Resolve Queue correctness and efficiency improvements: enqueue branch slot index, valid-entry flushing, and flush state retention to prevent re-evaluation. - FTQ handling simplification: remove newest_entry so last-entry instructions are not sent to backend, reducing target dependencies. - Control flow optimization: BPU independence and ResolveQueue IO/meta storage decoupling with register-based meta storage and removal of identifiedCfi, enabling autonomous BPU decision-making. Impact and tech signal: - Improved correctness and throughput across instruction scheduling paths. - Fewer backend interactions and better timing margins via reg-based storage. - Demonstrated hardware-software co-design, timing-aware optimization, and IO/memory decoupling. Repository: OpenXiangShan/XiangShan
October 2025
6 Commits • 3 Features
Oct 1, 2025Month 2025-10 highlights three major features/bug fixes delivered to OpenXiangShan/XiangShan, focused on correctness, efficiency, and control-flow reliability. The work reduces re-evaluation, cuts backend dependency needs, and improves timing-aware storage orchestration across the ResolveQueue, FTQ handling, and BPU-control flow paths. Key outcomes: - Resolve Queue correctness and efficiency improvements: enqueue branch slot index, valid-entry flushing, and flush state retention to prevent re-evaluation. - FTQ handling simplification: remove newest_entry so last-entry instructions are not sent to backend, reducing target dependencies. - Control flow optimization: BPU independence and ResolveQueue IO/meta storage decoupling with register-based meta storage and removal of identifiedCfi, enabling autonomous BPU decision-making. Impact and tech signal: - Improved correctness and throughput across instruction scheduling paths. - Fewer backend interactions and better timing margins via reg-based storage. - Demonstrated hardware-software co-design, timing-aware optimization, and IO/memory decoupling. Repository: OpenXiangShan/XiangShan
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 monthly work summary for OpenXiangShan/XiangShan focused on delivering critical pipeline improvements, stabilizing core paths, and strengthening frontend/backend collaboration. The work emphasized business value through improved instruction throughput, correctness, and system stability across the instruction fetch unit (IFU), branch resolution, and metadata handling between FTQ and BPU.
6 Commits • 2 Features
Sep 1, 2025September 2025 monthly work summary for OpenXiangShan/XiangShan focused on delivering critical pipeline improvements, stabilizing core paths, and strengthening frontend/backend collaboration. The work emphasized business value through improved instruction throughput, correctness, and system stability across the instruction fetch unit (IFU), branch resolution, and metadata handling between FTQ and BPU.
September 2025
August 2025
4 Commits • 1 Features
Aug 1, 2025Concise monthly summary for 2025-08: Focused on strengthening the Branch Prediction Unit (BPU) and redirect handling in OpenXiangShan/XiangShan. Implemented backend-informed redirect integration, speculation metadata management, and frontend-backend communication refactors to enable more accurate branch resolution. Key enhancements include linking redirect data to the BPU, and training the BPU with information provided by the backend when branches are resolved, while restricting training to mispredicts or identified branches. These changes lay the groundwork for higher instruction throughput and reduced mispredictions, with clearer data flows and maintainability.
August 2025
4 Commits • 1 Features
Aug 1, 2025Concise monthly summary for 2025-08: Focused on strengthening the Branch Prediction Unit (BPU) and redirect handling in OpenXiangShan/XiangShan. Implemented backend-informed redirect integration, speculation metadata management, and frontend-backend communication refactors to enable more accurate branch resolution. Key enhancements include linking redirect data to the BPU, and training the BPU with information provided by the backend when branches are resolved, while restricting training to mispredicts or identified branches. These changes lay the groundwork for higher instruction throughput and reduced mispredictions, with clearer data flows and maintainability.
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Quality Metrics
Correctness84.0%
Maintainability80.0%
Architecture80.0%
Performance81.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Scala
Technical Skills
ChiselScalabackend developmentbranch predictionfront end developmentfrontend developmenthardware description languageshardware designhardware simulationsoftware debuggingsoftware engineeringsystem architecturesystem design
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