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cs.LG2022
AMED: Automatic Mixed-Precision Quantization for Edge Devices
Moshe Kimhi, Tal Rozen, Avi Mendelson +1
Quantized neural networks are well known for reducing the latency, power consumption, and model size without significant harm to the performance. This makes them highly appropriate…
cs.LG2022
Bimodal Distributed Binarized Neural Networks
Tal Rozen, Moshe Kimhi, Brian Chmiel +2
Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer…