Triage incorrect MNN outputs and regressions by matching symptoms to focused debugging guides. Use backend comparisons, minimal reproductions, and controlled experiments to investigate memory issues, quantization, concurrency, numeric limits, caches, and inconsistent results across runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating garbled or degraded MNN output
• Comparing results across execution backends
• Tracking regressions after optimization changes

Find the right workflow for MNN CPU work, from investigating slow execution to implementing calculation routines and planning tests. Compare ARM, x86_64, and RISC-V backend structures before choosing the relevant guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing where to start an MNN CPU performance investigation
• Finding guidance for CPU kernel implementation
• Comparing ARM, x86_64, and RISC-V backend structures

Develop or port CPU kernels, the low-level calculation routines used by MNN. Follow staged implementation guidance, check data layout agreements, register functions, and verify correctness across processor instruction sets and numerical precision settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implementing or porting an MNN CPU kernel
• Adding support for a processor instruction set
• Changing packed data layouts or tile sizes

Find why MNN CPU inference is slow by examining threads, scheduling, memory layout, and the processor instruction path actually used. Operator-level measurements and comparisons with other inference frameworks help identify which layer needs improvement across ARM, x86_64, and RISC-V.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate slow CPU operators or LLM inference
• Diagnose thread count and memory usage anomalies
• Locate the cause of a performance regression

Optimize and troubleshoot MNN's Hexagon DSP backend, which runs computation on a dedicated processor. The workflow uses device measurements to identify bottlenecks, compare v79 and v81 architectures, verify correctness, and investigate crashes or memory usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding bottlenecks in Hexagon operators
• Checking changes on v79 and v81 devices
• Investigating cDSP crashes and device disconnects

Guide MNN Metal backend development and optimization, from finding performance bottlenecks to changing GPU computation, combining operators, and adjusting execution scheduling. A task-based document index directs work toward relevant implementation rules, benchmarks, and correctness checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding bottlenecks in Metal inference
• Adding or modifying Metal kernels
• Combining operators into one GPU dispatch