This company develops open-source computer graphics software for digital content creation across various applications. Their technologies support physical simulation, augmented reality, and visual effects, providing accessible graphics tools for creators.
Funding
$50M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Creating high-performance computer graphics, numerical computations, and AI applications often requires specialized knowledge and complex programming, hindering accessibility for many developers and researchers. Existing tools can be difficult to learn and may not fully leverage parallel computing architectures for optimal performance.
Solution
Taichi is a domain-specific, open-source programming language embedded in Python, designed to simplify the development of high-performance parallel applications. It allows users to write code that automatically translates into optimized machine code for CPUs or GPUs, achieving speeds comparable to C++ or CUDA. Taichi's intuitive syntax and automatic parallelization features significantly lower the barrier to entry for creating advanced simulations, rendering engines, and computer vision systems.
Target Audience
The primary users are researchers, developers, and students in fields such as computer graphics, physics simulation, robotics, and artificial intelligence who require a high-performance, easy-to-use programming language for creating parallel applications.
Features
- Embedded in Python with a familiar syntax, enabling a low learning curve for Python developers
- Just-in-time (JIT) compiler that automatically translates Python code into optimized machine code for parallel execution on CPUs or GPUs
- Support for automatic differentiation, facilitating the development of differentiable programs for machine learning and optimization tasks
- Flexible SNode system for experimenting with different memory layouts to maximize performance without rewriting computation code
- Spatially sparse data structures that enable efficient computation and memory usage in simulations with large empty regions
- Ahead-of-time (AOT) compilation mode for deploying applications on platforms without Python, including PCs, mobile devices, and web browsers
- Seamless interoperability with popular Python frameworks such as NumPy, PyTorch, matplotlib, and pillow