Neural network processor designed for edge devices
Overview
Kneron NPU IP Series are neural network processors that have been designed for edge devices. These processors provide high computing performance with low power consumption and are small in size. Kneron NPU IP Series can be applied to smart homes, smart surveillance, smartphones, and wearable devices that have high requirement for low power and space. The entire product consumes under 0.5W and can even drop below 5mW for specific applications.
Key Features
- High energy efficiency
- All series reach higher than 1.5 TOPS/W.
- Support mainstream deep learning frameworks
- Caffe, Keras, TensorFlow, and ONNX.
- Low power consumption
- Under 0.5W and can be less than 5 mW for specific applications.
- An integrated AI solution
- Include hardware IP, compiler, and model compression.
- Deep compression technology
- Compresses not only models but also data and coefficients during computing to reduce memory use.
- Filter decomposition and convolution acceleration
- Divides a large-scale convolutional block into a number of smaller ones for parallel computing and then integrates and accelerates the results.
- CNN model support optimization
- Supports diverse CNN models, including Vgg16, Resnet, GoogleNet, YOLO, Tiny YOLO, Lenet, MobileNet, and Densenet with model specific performance optimization.
- Interleaving computation architecture
- Enable parallel convolution computing and pooling to improve overall performance. The convolution layer can support both 8- and 16-bits fixed points concurrently.
- Adaptive data structure
- Adjusts the data structure dynamically to improve MAC efficiency depending on changing demand.
- Dynamic storage resource configuration
- Allows more efficient resource allocation between shared memory and operational memory. Increases storage resource utilization without affecting performance.
Technical Specifications
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