AI Chip Technology
The Optimal Choice for Edge Computing and Development Efficiency
Specifically designed for Edge AI applications, it features 4 TOPS computing power, supports multi-precision model execution, is compatible with mainstream deep learning frameworks, and offers a comprehensive AI development toolkit and SDK to streamline processes and enhance efficiency.
Exceptional Computing Performance
- Equipped with a high-performance Neural Processing Unit (NPU) delivering up to 4 TOPS, significantly accelerating deep learning model inference.
- Integrated with a dedicated Vision Processing Unit (VPU), optimized for image analysis and computer vision applications, supporting large-scale real-time computations.
Multi-Framework and Model Compatibility
- Supports mainstream AI frameworks, including PyTorch, ONNX, TensorFlow, TensorFlow Lite,Keras, Caffe, and more, reducing learning curves and integration costs.
- Offers extensive model support, including classification models, object detection models, skeleton detection models, image processing models, and language models
Efficient AI Compiler
- Automated computing resource allocation technology optimizes performance across TPUs, VDSPs, and CPUs to achieve peak efficiency.
- Supports model porting and optimization for frameworks like TensorFlow and PyTorch, reducing development and deployment time.
Developer-Friendly
- Preloaded models enable quick configuration and deployment on the chip, supporting applications such as classification, detection, image segmentation, and voice commands.
- Offers a user-friendly SDK and simple APIs for device initialization, model loading, inference data transmission, and result retrieval.
Energy-Efficient Design and Flexible Scalability
- Features a low-power architecture that balances high performance with energy efficiency, meeting the demands of edge computing scenarios.
- Supports a wide range of devices, from IoT equipment to high-performance edge servers, flexibly adapting to various deployment requirements.
Continuous Technological Evolution
- Currently supports a wide range of models and architectures, including MobileNetV1/V2/ V3, InceptionNetV1/V2, and EfficientNetV1/V2, with plans to continuously expand support for emerging technologies, ensuring developers remain at the forefront of innovation.
- Regularly updates frameworks and tools to provide stable, fast, and long-term technical support.
Choose ACS AI chips
to accelerate innovation and unlock the full potential of Edge AI applications.