Case study
C Tensor / Neural Network Library
A low-level C project for tensor operations and neural network primitives with attention to memory and implementation mechanics.
Overview
A systems programming project exploring tensor data structures, neural network building blocks, and the low-level mechanics behind higher-level AI tooling.
Problem
Modern ML libraries hide most of the memory layout, numerical, and API design decisions that make tensor computation work.
What I Built
- Tensor-oriented data structures and primitive operations.
- Neural network building block experiments.
- A visual matrix playground for inspecting tensor values and parameter controls.
Technologies Used
- C
- Memory management
- Numerical computing
- Neural network primitives
Key Technical Challenges
- Designing clear APIs around manual memory management.
- Keeping shape handling and allocation behavior understandable.
- Balancing educational clarity with performance-minded implementation choices.
Links
What It Demonstrates
- Low-level engineering
- AI infrastructure curiosity
- Memory management
- Numerical systems design
Current Status
Public source is linked above; this page highlights the project goals, implementation focus, and visual artifact.