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• Detector data is particularly well suited for representation in the form of images

• e.g. calorimeter trigger information often transmitted as pixelated energy

• Convolutional neural networks provide a well-studied and appropriate machine learning (ML) approach to classifying images of this form

3arXiv:1707.08600

https://arxiv.org/abs/1707.08600

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• DNNDK – Deep Neural Network Development Kit

• hls4ml – High Level Synthesis for ML

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Testing model

Extremely preliminary

https://www.xilinx.com/products/design-tools/ai-inference/ai-developer-hub.html#edgehttps://hls-fpga-machine-learning.github.io/hls4ml/

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5https://www.xilinx.com/support/documentation/user_guides/ug1327-dnndk-user-guide.pdf

https://www.xilinx.com/support/documentation/user_guides/ug1327-dnndk-user-guide.pdf

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6https://www.xilinx.com/support/documentation/ip_documentation/dpu/v1_2/pg338-dpu.pdf

https://www.xilinx.com/support/documentation/ip_documentation/dpu/v1_2/pg338-dpu.pdf

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ResNet50 image classification example – much bigger and more

complicated than our network

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9https://hls-fpga-machine-learning.github.io/hls4ml/CONCEPTS.html

https://hls-fpga-machine-learning.github.io/hls4ml/CONCEPTS.html

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10Image and work by Jack Huang, UChicago Undergrad

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