Tal Rozen
YOU?
Author Swipe
View article: AMED: Automatic Mixed-Precision Quantization for Edge Devices
AMED: Automatic Mixed-Precision Quantization for Edge Devices Open
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 for systems with limited resources and low power capacity.…
View article: Bimodal-Distributed Binarized Neural Networks
Bimodal-Distributed Binarized Neural Networks Open
Binary neural networks (BNNs) are an extremely promising method for reducing deep neural networks’ complexity and power consumption significantly. Binarization techniques, however, suffer from ineligible performance degradation compared to…
View article: AMED: Automatic Mixed-Precision Quantization for Edge Devices
AMED: Automatic Mixed-Precision Quantization for Edge Devices Open
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 for systems with limited resources and low power capacity.…
View article: Bimodal Distributed Binarized Neural Networks
Bimodal Distributed Binarized Neural Networks Open
Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer from ineligible performance degradation compared to their …
View article: NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization Open
Convolutional Neural Networks (CNNs) are very popular in many fields including computer vision, speech recognition, natural language processing, etc. Though deep learning leads to groundbreaking performance in those domains, the networks u…