4 citations · 7 across the 5 of their papers we have counts for
3 papers · 1 filter
Towards Optimal Compression: Joint Pruning and Quantization
Ben Zandonati, Glenn Bucagu, Adrian Alan Pol +3
Model compression is instrumental in optimizing deep neural network inference on resource-constrained hardware. The prevailing methods for network compression, namely quantization…
FIT: A Metric for Model Sensitivity
Ben Zandonati, Adrian Alan Pol, Maurizio Pierini +2
Model compression is vital to the deployment of deep learning on edge devices. Low precision representations, achieved via quantization of weights and activations, can reduce infer…
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…