Architecture Features
Designed to accelerate convolutions (including Transposed CONV, Dilated CONV, and CONV3D), matrix multiplication, interpolation, and ROIs.
Highly efficient storage and computing technologies.
Support INT8/INT16 matrix computation, as well as FP16/BFP16 vector processing.
Key activation functions are natively supported. Customized activation functions can be supported via math instructions.
Featured Technology
Optimized for CNN-based algorithms and vision tasks. Also support general-purpose DNN accelerations.
Support model compression and quantization.
High power-efficiency with low latency.
Programmable and easy to use.
Adaptive software stack, supporting various frameworks, such as TensorFlow, MXNet, Caffe, ONNX, etc.
Performance Comparison
Resnet50 v1 Performance (Image/Second):
Resnet50 v1 Efficiency (Image/Second/Watt)：
