SOLUTION BRIEF

ZEBRA™ BY MIPSOLOGY: ACCELERATING NEURALNETWORK INFERENCEINTRODUCTIONFPGAs Ideally suited for Inference Acceleration
FPGAs are full of basic computing elements and filled with memories, ideally suitable forhigh performance and low latency CNN inference computing. They are reprogrammable at thehardware level allowing for continual adaptation. But FPGA programming can be challenging.

SOLUTION OVERVIEW
Zebra Software Accelerates Inference
Zebra is the ideal compute engine to accelerate CNN inference. Zebra seamlessly replaces
CPUs/GPUs to compute any neural network on an FPGA faster, with lower power consumption,and at lower cost.
Simply type a single Linux command. No knowledge of FPGA technology, compilation,or any changes to the environment or the application are required.
Zebra lets AI engineers focus on application development, while enjoying unmatchedperformance.

AMD ADVANTAGE• Delivers high performance from edge to cloud• Supports most popular deep learning
frameworks and broad range of CNN inference• Easy to use a single Linux command replaces
costly CPU or GPU inference nodes• Zero Neural Networks Change• Zero New Training• Zero Line of Code to Add• Zero FPGA Knowledge• Zero FPGA Compilation• Zero Transition Effort•… In Short, ZERO Effort

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SOLUTION BRIEF

KEY BENEFITSThe Fastest Inference

No Changes to the Software Environment• Computes neural networks highest speed with lowest latency• Not a single line of code must be changed in the application

Supports All Neural Networks

Scalable, Flexible and Adaptable• Accelerates any CNN, including user defined neural networks• Easy replacement of GPUs or complement CPUs

Extremely Simple to Use• Deploying Zebra is a “Plug & Play” process

SOLUTION DETAILSNeural Networks

Supported Frameworks• Supports CNN without modification• TensorFlow, PyTorch , ONNX, and many more

• Delivered with tested networks: GoogLeNet V1, Inception V3,Inception V4, VGG16, VGG19, ResNet50, ResNet152, YoloV1, YoloV2,YoloV3, Tiny YoloV2, Tiny YoloV3, VDSR, SSD, MobileNet• No change to source code required• Accelerated layers: convolution, fully connected, max/average
pooling, concat, batch norm, scale, add eltwise, reorg, up sampling,
depth to space, reduce mean, dilated convolution, squeeze,
separable depth wise, clip to value, relu, leaky relu, relu6Precision• 8 bit• Automatic proprietary quantization

Migration from GPU or CPU• Automatic split of graph• Single or multiple outputs• Trained parameters from GPU/CPU training without changes• Up to 3.2 billion weights• Up to 1360x1360x3 input
images• No proprietary training or re training needed, and no pruning required• Up to 24 simultaneous
independent users• Similar accuracy as FP32

• Up to 1 million layers• Unbounded number of convolutions

RESULTS• Usable immediately

Power & Cooling• From few watts in the field to 140W in data centers

Zebra’s quantization ensures results stay accurate and does not require retraining the NN.

NEXT STEPS• Learn more about AMD VCK5000 Development Card xilinx.com/products/boards-and-kits/vck5000

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