Machine Learning Accelerator

Seamless Deployment, Broad Network Support, Power Efficient


solution brief

Seamless Deployment:

  • Same software environment as CPU/GPU
  • No application software changes required
  • No new training required
  • U.2 Small Form Factor (SFF8639)

Broad Network Support:

  • Works under Caffe, Caffe2, TensorFlow, MXNET
  • Same CPU/GPU-based trained neural network with similar accuracy without any change

Power Efficient:

  • Scalable and Lower Power than GPUs
  • Ideal for Edge or Data Center Applications


Network Performance (Frames/Sec)
Caffenet 1715
Googlenet 561
Inception v3 143
Inception v4 76
Resnet50 239
Resnet152 120
Yolo v1 24
Yolo v2 44

Seamless Deployment, Broad Network Support, Power Efficient

No longer does the CPU have to be the center of a system. Data centric solutions are quickly emerging to unlock the value in Big Data and Fast Data by using purpose-built architectures. One important technology which can extract insights from data is Machine Learning.

Western Digital partnered with Mipsology to provide a complete, easy to adopt and power efficient Machine Learning Accelerator based on the Zebra platform


Seamless Deployment

Broad Support

Neural Networks

  • Tested with many pretrained networks: AlexNet, CaffeNet, GoogLeNet V1, Inception V3, Inception V4, VGG16, VGG19, ResNet50, ResNet152, Nin, Yolo, SSD…etc.
  • Supports custom CNN without modification
  • Supported layers: Convolutions, Fully Connected, Max/Average Pooling, Concat, LRN, Relu, Softmax, Batch Norm, Scale, Eltwise, etc
  • Up to 1 billion weights in a single network
  • Up to 1 million layers
  • Up to 200,000 filters per convolution

Supported Frameworks

  • Caffe, Caffe2, MXNet or TensorFlow
  • No code change required


  • 8-bit or 16-bit integers with automatic quantization
  • No mandatory pruning

Migration from GPU or CPU

  • Trained parameters from GPU training without changes
  • No proprietary training or re-training needed
  • Similar accuracy as GPU or CPU
  • Switch networks instantaneously without reprogramming FPGA
  • Supports multiple users, multiple networks in parallel, multiple boards

Hardware format, power & cooling

  • U.2 Small Form Factor (SFF8639)
  • Typical power less than 20W, passive cooling
  • Single BGA package planned

For further details contact

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