Add new operators to MNN through a staged workflow covering data definitions, output shapes, computation, and tests. It prioritizes combining existing operators, with CPU implementations and additional hardware backends used when needed.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
- Adding a new operator to MNN
- Implementing output shape calculations
- Combining existing operators for new operations
alibaba/MNN☆ 1.6만2026년 10월 16일 업데이트
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
alibaba/MNN☆ 1.6만2026년 10월 16일 업데이트
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
alibaba/MNN☆ 1.6만2026년 10월 16일 업데이트
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
alibaba/MNN☆ 1.6만2026년 10월 16일 업데이트
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
alibaba/MNN☆ 1.6만2026년 10월 16일 업데이트
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
alibaba/MNN☆ 1.6만2026년 10월 16일 업데이트