Daehyun Ahn
Co-founder
SqueezeBits
SqueezeBits offers a platform for compressing neural networks, enabling efficient deployment on resource-constrained devices. Their tools facilitate the design, training, and optimization of smaller, faster AI models.
- Artificial Intelligence
- Developer Tools
- Software Only
Updated 5 months ago
Funding
Raised to date
$1.9MRaised 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.
Funding rounds are not available yet.
Founders
Hyungjun Kim
Co-founder
Taesu Kim
Co-founder
Product
Problem
Operating large AI models incurs substantial computational costs, leading to high operational expenses and latency issues, hindering real-time applications and on-device deployment. Existing solutions often require specialized hardware or extensive manual optimization, increasing complexity and development time.
Solution
SqueezeBits offers an AI compression toolkit, OwLite, that streamlines the process of optimizing and deploying efficient neural networks. By applying advanced AI compression techniques such as quantization, OwLite reduces the size of AI models, enabling faster inference speeds and lower computational costs. The toolkit simplifies the application of these techniques to pre-existing machine learning models, allowing for seamless integration into existing PyTorch training scripts with just a few lines of code. SqueezeBits' technology facilitates the deployment of AI models on resource-constrained devices, broadening the scope of AI applications while ensuring data security and privacy by keeping training data and model weights on the user's server.
Target Audience
SqueezeBits targets AI developers and businesses seeking to reduce the computational costs, latency, and size of their AI models for efficient deployment on cloud infrastructure or edge devices.
Features
- Low-code AI model compression toolkit for PyTorch models
- Quantization-aware training for performance recovery in lightweight models
- Seamless integration into existing PyTorch training scripts
- Compatibility between PyTorch, ONNX, and TensorRT engines
- Customizable quantization settings for tailored compression
- Secure processing: training data and model weights remain on the user's server
- Support for various model types