February 2026: Stabilized Folly build across modern toolchains by fixing a template-dependent name lookup issue in F14Table.h, improving reliability and reducing developer friction for GCC 13.3.0 on Ubuntu 24.04.

December 2025: Delivered broad RVV intrinsics–driven performance optimizations in Alibaba/MNN across image processing and neural network primitives, enhancing throughput for CV/ML workloads while ensuring robust cross-platform builds on Linux and QNX. Implemented extensive vectorized paths (max/min, softmax, relu with slope, top1, bilinear/cubic resize, image blitter, color conversions, convolution/Strassen) and added cross-platform guards to prevent redefinition during compilation. Resulting improvements include faster inference, reduced latency, and improved portability.

Month: 2025-11 Overview: Implemented RVV intrinsic-based optimizations in MNN to accelerate common data handling and core math paths, enabling better utilization of RVV-capable hardware for on-device inference.

September 2025: Major performance optimization for large-matrix operations in the alibaba/MNN repository using RISC-V Vector Intrinsics. Replaced scalar implementations of MNNMatrixAdd, MNNMatrixSub, and MNNMatrixMax with RVV-enabled versions, delivering substantial throughput improvements on large matrices. Benchmarks show up to 13.48x speedup for MatrixMax and over 6x for MatrixAdd and MatrixSub. The change enhances on-device inference speed and hardware utilization for RV-enabled hardware, enabling faster model runs and more efficient energy usage on edge devices.

August 2025 performance summary for alibaba/MNN: Delivered RISC-V Vector (RVV) acceleration for matrix multiplication and related packing operations, expanding high-performance inference to RVV-enabled devices. Implemented build macro MNN_USE_RVV, RVV-specific MNNMatrixProd optimizations, improved packing paths for RVV, and infrastructure fixes to ensure RVV backends initialize cleanly without conflicting with CPUBackend. Standardized function naming to support scalable numerical tasks and smoother integration across backends. Fixed key issues including removal of redundant CPUBackend_creation and corrected function naming, enhancing stability and maintainability.