In April 2025, Atal contributed to the Tencent/ncnn repository by developing a performance optimization for the BNLL (bounded non-linear layer) operation targeting RISC-V architecture. The work focused on enhancing inference speed for both float and half-precision data types, utilizing C++ and advanced vectorization techniques. Atal implemented RISC-V-specific enhancements that leveraged parallel programming and architecture features to improve computational efficiency. The optimization was validated through targeted benchmarks and integration tests, ensuring robust performance gains. This contribution addressed the need for faster neural network inference on RISC-V platforms, demonstrating depth in low-level optimization and a strong understanding of hardware-specific acceleration.