AndesAIRE™ AnDLA™ I350

 AndesAIRE™ AnDLA™ I350 is a deep learning accelerator (DLA) designed to enable high performance-efficient and cost-sensitive AI solutions for edge and end-point inference. It supports popular deep learning frameworks, such as TensorFlow Lite, PyTorch, and ONNX, and performs versatile neural network operations such as convolution, fully-connect, element-wise, activation, pooling, channel padding, upsample, concatenation, etc. in the int8 data type. It also features an internal Direct Memory Access (DMA) and local memory, utilizing the best computing power of the hardware engines. Operation fusion techniques are also adopted in the AnDLA™ I350 to perform most common operator sequences more efficiently. The key configurable parameters of AnDLA™ I350 include the MAC number from 32 to 4096, and SRAM size from 16KB to 4MB, and provide flexible computing power from 64 GOPS to 8 TOPS (at 1 GHz) for a wide range of applications. 

The product is available in an early-adopter program now. 

• Configurable MACs from 32 to 4096 (INT8)
• Maximum performance 8 TOPS at 1GHz
• Configurable local memory: 16KB to 4MB
• Multi-dimension DMA
• Four 64-bit AXI bus interfaces
• NN type: CNN inference
• NN models:
  • Image and Video: AlexNet, VGG-16/19, MobileNet-v1/v2/v3, ResNet-8/50, Tiny YOLO v1/v2, YOLO v1/v2/v3/v4/v5, SSD MobileNet v1/v2, Inception v2, EfficientNet-lite, MobileFaceNet, BlazeNet
  • Speech/Voice and audio: LSTM, RNN, GRU
• Operators: Conv2d, depthwise convolution, pointwise convolution, transpose convolution, dilated convolution, element-wise (add, sub, mul), fully-connected, activation (ReLU, leaky ReLU, sigmoid, Tanh, ReLU6, SiLU), pooling (max, ave), upsample, concatenation, batch normalization, channel padding
• Operator fusion
• NHWC data format