"Neural Network Compression for Noisy Storage Devices" will appear at the ACM Transactions on Embedded Computing Systems (TECS):
We propose ways to provide robustness to neural networks against noise present in storage or communication environments.
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We use an analog storage technology (PCM) as an example to show that the noise added by the PCM cells is detrimental to the performance of neural networks and that we can recover full accuracy with our robust coding strategies.
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Our strategies can be applied in other scenarios beyond this example that add noise to the neural network weights. If you're not interested in the analog storage part, but want to learn how to make neural networks more robust to noise in general, you can skip Section 2.
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