

Zetic.ai
Overview :
ZETIC.ai offers a revolutionary on-device AI solution that leverages NPU technology to help businesses reduce reliance on GPU servers and AI cloud services, significantly cutting costs. It supports any operating system, processor, and target device, ensuring that no core functionalities are lost during the conversion process while achieving optimal performance and maximum energy efficiency. Additionally, it enhances data security by processing data internally on the device, thus avoiding external leakage risks.
Target Users :
ZETIC.MLange Beta 0.2 is designed for AI companies concerned about high AI costs, data breach risks, and considering a shift to on-device AI. It is particularly suited for businesses seeking cost-effectiveness, rapid deployment, and data security.
Use Cases
A mobile application company used ZETIC.MLange to deploy AI models on smartphones, reducing reliance on cloud services.
A medical device manufacturer utilized ZETIC.MLange to implement AI diagnostic functions on the device, enhancing data processing speed and security.
An IoT device company ran AI on local devices via ZETIC.MLange, lowering operational costs and improving user experience.
Features
Model Preparation: Ensure confidentiality of the AI model and data.
Performance Analysis: Optimize the model for all target devices to improve efficiency and reduce latency.
Target Library Implementation: Convert the model into deployable mobile libraries.
On-device Application Implementation: Deploy directly on-device, eliminating server dependence and costs.
Automated Pipeline: Streamline the implementation process for on-device AI models.
Hardware-specific Optimization: Ensure optimal performance across different devices.
On-device AI Runtime Library: Provide necessary library support for the on-device AI operation.
How to Use
1. Prepare the AI model and data, ensuring confidentiality.
2. Use ZETIC.MLange's performance analysis tools to optimize the model.
3. Convert the optimized model into a mobile library for the target device.
4. Deploy the AI application on the target device to enable on-device AI.
5. Utilize the automated pipeline provided by ZETIC.MLange to simplify the deployment process.
6. Perform specific optimizations based on the hardware characteristics of the target device.
7. Integrate the on-device AI runtime library to ensure stable operation of the AI model.
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