[P] Aakaar – A custom deep learning framework built from scratch in C++/CUDA
![[P] Aakaar – A custom deep learning framework built from scratch in C++/CUDA](https://external-preview.redd.it/vuCtmvzB96il2o9N92qwApjR6KcIGIh9vjSIEe-C0LY.png?width=140&height=70&auto=webp&s=313ef14c2c2c90753c907fa7a9df5c9518691187 "[P] Aakaar – A custom deep learning framework built from scratch in C++/CUDA") |
I built Aakaar to completely strip away the black-box abstraction of modern AI infrastructure (like PyTorch) and force strict, explicit interaction with hardware realities. Technical Architecture:
Benchmarks (EMNIST): To see if this was structurally viable and not just a toy matrix library, I ran a 5-epoch training loop on the EMNIST dataset to benchmark it directly against PyTorch on my local machine (RTX 4060, 8GB VRAM).
Convergence parity was absolute, and Aakaar slightly edged out in speed due to the low-overhead C++ optimizer steps bypassing the standard Python overhead. The Hardest Challenge: Mapping abstract mathematical shapes to physical GPU hardware and strictly tracking memory layouts during the backward passes without relying on an automated autograd graph. Links:
I would appreciate any feedback from researchers or engineers here, especially regarding potential optimization bottlenecks in the CUDA kernels or the C++ memory management approaches. submitted by /u/AaravAggarwal |
