47 citations · 71 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 24 cited
Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)
Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel +3
While neural networks have advanced the frontiers in many machine learning applications, they often come at a high computational cost. Reducing the power and latency of neural netw…
cs.CV2019★ 47 cited
QKD: Quantization-aware Knowledge Distillation
Jangho Kim, Yash Bhalgat, Jinwon Lee +2
Quantization and Knowledge distillation (KD) methods are widely used to reduce memory and power consumption of deep neural networks (DNNs), especially for resource-constrained edge…