3 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 2 cited
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs
Dingyi Dai, Yichi Zhang, Jiahao Zhang +4
Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix mult…
cs.LG2019★ 3 cited
OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators
Ritchie Zhao, Jordan Dotzel, Zhanqiu Hu +3
Outliers in weights and activations pose a key challenge for fixed-point quantization of neural networks. While they can be addressed by fine-tuning, this is not practical for ML s…