1 code implementation • 19 Jun 2023 • Wenqi Jiang, Shigang Li, Yu Zhu, Johannes De Fine Licht, Zhenhao He, Runbin Shi, Cedric Renggli, Shuai Zhang, Theodoros Rekatsinas, Torsten Hoefler, Gustavo Alonso
Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluating vector similarities between encoded query texts and web documents.
no code implementations • 8 Dec 2020 • Sung-En Chang, Yanyu Li, Mengshu Sun, Runbin Shi, Hayden K. -H. So, Xuehai Qian, Yanzhi Wang, Xue Lin
Unlike existing methods that use the same quantization scheme for all weights, we propose the first solution that applies different quantization schemes for different rows of the weight matrix.
no code implementations • 16 Sep 2020 • Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Runbin Shi, Xue Lin, Yanzhi Wang
To tackle the limited computing and storage resources in edge devices, model compression techniques have been widely used to trim deep neural network (DNN) models for on-device inference execution.
1 code implementation • ICLR 2020 • Junjie Liu, Zhe Xu, Runbin Shi, Ray C. C. Cheung, Hayden K. -H. So
We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds.
1 code implementation • 23 Aug 2019 • Tong Geng, Ang Li, Runbin Shi, Chunshu Wu, Tianqi Wang, Yanfei Li, Pouya Haghi, Antonino Tumeo, Shuai Che, Steve Reinhardt, Martin Herbordt
Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio.