eNeural Technologies introduces its Self-Learning Design Methodology, a groundbreaking approach to AI model development. As an AI SW/HW design service provider, eNeural Technologies focuses on delivering embedded AI models of the highest quality. Their in-house toolchain automates the entire AI process flow, from labeling and modeling to training, augmentation, pruning, and quantization. With the addition of the Self-Learning Design Methodology, the toolchain utilizes a small number of labeled data to train a baseline inference model. The toolchain then leverages unlabeled data to quickly converge into a highly accurate model. This methodology has resulted in more accurate models in significantly faster time-to-market, benefiting various user applications.
- Automated AI Process Flow - Streamlines the AI model development process by automating labeling, modeling, training, augmentation, pruning, and quantization.
- Self-Learning Design Methodology - With a small number of labeled data, the methodology trains a baseline inference model. It then utilizes unlabeled data to rapidly converge into a highly accurate model.
- Improved Time-to-Market - The Self-Learning Design Methodology enables faster development timeliness, resulting in more accurate models in 6 times faster time-to-market.
- Quality and Lightweight Inference Models - Produces high-quality and lightweight inference models suitable for AI Systems-on-Chip (SoCs) with 8-bit or smaller integer Neural Processing Units (NPU).
- Versatile Applications - Applicable to various user applications, the Self-Learning Design Methodology enhances accuracy and efficiency in AI model development.
eNeural Technologies introduces its Self-Learning Design Methodology, a groundbreaking approach to AI model development. As an AI SW/HW design service provider, eNeural Technologies focuses on delivering embedded AI models of the highest quality. Their in-house toolchain automates the entire AI process flow, from labeling and modeling to training, augmentation, pruning, and quantization. With the addition of the Self-Learning Design Methodology, the toolchain utilizes a small number of labeled data to train a baseline inference model. The toolchain then leverages unlabeled data to quickly converge into a highly accurate model. This methodology has resulted in more accurate models in significantly faster time-to-market, benefiting various user applications.
- Automated AI Process Flow - Streamlines the AI model development process by automating labeling, modeling, training, augmentation, pruning, and quantization.
- Self-Learning Design Methodology - With a small number of labeled data, the methodology trains a baseline inference model. It then utilizes unlabeled data to rapidly converge into a highly accurate model.
- Improved Time-to-Market - The Self-Learning Design Methodology enables faster development timeliness, resulting in more accurate models in 6 times faster time-to-market.
- Quality and Lightweight Inference Models - Produces high-quality and lightweight inference models suitable for AI Systems-on-Chip (SoCs) with 8-bit or smaller integer Neural Processing Units (NPU).
- Versatile Applications - Applicable to various user applications, the Self-Learning Design Methodology enhances accuracy and efficiency in AI model development.