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Found 2 papers, 2 papers with code
Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks
We propose a method of training quantization thresholds (TQT) for uniform symmetric quantizers using standard backpropagation and gradient descent.
Training a Fully Convolutional Neural Network to Route Integrated Circuits
After 15 fully convolutional stages followed by a score comparator, the network outputs 8 layout layers (corresponding to 4 route layers, 3 via layers and an identity-mapped pin layer) which are then decoded to obtain the routed layouts.
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