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 through vectorized implementations that leverage RISC-V-specific features. Using C++ and applying expertise in parallel programming and vectorization, Atal introduced architectural enhancements that improved computational efficiency and broadened hardware support. The optimization was validated with targeted benchmarks and integration tests, demonstrating a deep understanding of neural network primitives and low-level hardware acceleration. No bug fixes were reported during this period, reflecting focused feature development.