OpenXiangShan-Nanhu/Nanhu-V5
Nov 2024 – Nov 2024
1 Month active
Languages Used
Scala
Technical Skills
Computer ArchitectureDigital Logic DesignHardware DesignVerilog/Chisel
denghaiwen
PROFILE
Denghaiwen
Worked on the OpenXiangShan-Nanhu/Nanhu-V5 repository to enhance CPU performance and reliability by tuning the instruction fetch and predictor stack. Focused on microarchitectural improvements, this developer enlarged the Instruction Fetch Buffer and optimized the TAGE predictor by reducing the number of banks, using Verilog, Chisel, and Scala. Addressed a bug in TAGE table initialization by refining reset logic and handling valid bits correctly, which improved predictor startup behavior. The work resulted in higher instruction throughput, reduced mispredictions, and lower energy per instruction, while maintaining clear Git traceability and disciplined regression practices to support ongoing maintainability and workload performance.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
60
Activity Months1
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Work History
November 2024
2 Commits • 1 Features
Nov 1, 20242024-11 monthly summary for OpenXiangShan-Nanhu/Nanhu-V5 focusing on performance improvements and reliability enhancements in the CPU fetch/predictor stack. Delivered a feature and a bug fix that together improve instruction fetch throughput, reduce mispredictions on startup, and strengthen predictor initialization. Demonstrated strong microarchitectural tuning, regression discipline, and clear Git traceability. Business value is higher IPC, better workload performance, and lower energy per instruction, with reduced maintenance risk.
2 Commits • 1 Features
Nov 1, 20242024-11 monthly summary for OpenXiangShan-Nanhu/Nanhu-V5 focusing on performance improvements and reliability enhancements in the CPU fetch/predictor stack. Delivered a feature and a bug fix that together improve instruction fetch throughput, reduce mispredictions on startup, and strengthen predictor initialization. Demonstrated strong microarchitectural tuning, regression discipline, and clear Git traceability. Business value is higher IPC, better workload performance, and lower energy per instruction, with reduced maintenance risk.
November 2024
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Scala
Technical Skills
Computer ArchitectureDigital Logic DesignHardware DesignVerilog/Chisel
Repositories Contributed To
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