151 citations · 437 across the 32 of their papers we have counts for
4 papers · 1 filter
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…
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
Chenhui Deng, Zhiqiang Zhao, Yongyu Wang +2
Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding m…
Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets
Cunxi Yu, Zhiru Zhang
Physical design process commonly consumes hours to days for large designs, and routing is known as the most critical step. Demands for accurate routing quality prediction raise to…
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2
Quantization can improve the execution latency and energy efficiency of neural networks on both commodity GPUs and specialized accelerators. The majority of existing literature foc…