Research Project by NileAGI

EllipseDeep Learning in C

A lightweight, high-performance numerical computing library for deep learning written in pure C. Inspired by PyTorch, micrograd, and XLA.

Pure C
Implementation
Lightweight
Design
AOT
Compiled
Ellipse Logo

Why Ellipse?

Built for performance, designed for simplicity

Lightweight Design

Focused on simplicity, providing core deep learning operations without heavy dependencies. Perfect for embedded systems and resource-constrained environments.

Pure C Implementation

Built entirely in C for maximum portability and optimized low-level manipulation. No external dependencies, runs anywhere C runs.

Advanced Features

Includes automatic differentiation, flexible tensor operations, lazy backpropagation, and a modular architecture for easy extension.

Performance Comparison

Real-world benchmarks comparing Ellipse and PyTorch

Faster

Execution Time

Ellipse~2.1s
PyTorch~3.8s
Lower

Memory Usage

Ellipse~45MB
PyTorch~280MB
Smaller

Binary Size

Ellipse~2.1MB
PyTorch~850MB
Optimized

CPU Efficiency

Ellipse~85%
PyTorch~72%
Ellipse

Ellipse

Lightweight & Fast

Execution Time2.1s
Memory Usage45MB
Binary Size2.1MB
Dependencies0
Ellipse Performance Output
Py

PyTorch

Industry Standard

Execution Time3.8s
Memory Usage280MB
Binary Size850MB
Dependencies50+
PyTorch Performance Output

Key Takeaways

Ellipse delivers comparable performance with significantly lower resource requirements

6.2x
Smaller Memory
405x
Smaller Binary
1.8x
Faster Execution

Who is This For?

Perfect for developers who want to understand and control every aspect of deep learning

Learning & Education

Understand deep learning libraries from the ground up with clean, readable C code

Resource-Limited

Run neural networks in embedded systems, IoT devices, and low-resource environments

Custom ML Operations

Prototype and implement custom ML operations in C with full control over the stack

Ready to Get Started?

Ellipse is open source and welcomes contributions from the community. Join us in building the future of lightweight deep learning.