PPT-Low-latency RNN inference with Cellular Batching

Lowlatency RNN inference with Cellular Batching Lingfan Yu joint work with Pin Gao Yongwei Wu Jinyang Li New York University Tsinghua University 1 Deep Neural Network is popular Google Translate reportedly processes 100 billion words every day. 11 3266 2453 4819 3483 4819 3483 3604 3604 Anthem Dentegra Dentegra Family Plan Type High Low High Low Low High Low High Low High Low Low Low Diagnostic Preventive DP 100 80 100 100 100 100 100 100 100 100 100 100 100 Basic Services 75 60 80 50 60 8 Shilen Patel. Duke University. This work licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License.. 2. Contents. Demo JNDI source adapter. Batching. Paging. 3. Batching in Subject API. M. achine . T. ranslation. EMNLP. ’. 14 paper by . K. yunghyun. . C. ho, et al.. Recurrent Neural Networks (1/3). 2. Recurrent Neural . Networks (2/3). A variable-length sequence . x . = (x. 1. , …, . Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. to Parallel Batching-based Updates. Youngsun. . Kwon. and . Sung-. eui. Yoon. KAIST, South Korea. ICRA 2017 Workshop on. Robotics and Vehicular Technologies for Self-Driving Cars. It is essential to update map representation in. Keqiang He, . Weite. Qin, . Qiwei. Zhang, . Wenfei. Wu. , . Junjie. Yang, Tian Pan, . Chengchen. Hu, Jiao Zhang, Brent Stephens, Aditya . Akella. , Ying Zhang. 1. Bandwidth Allocation in Clouds . Li Deng . Deep Learning Technology Center. Microsoft AI and Research Group. Invited Presentation at NIPS Symposium, December 8, 2016. Outline. Topic one. : RNN versus Nonlinear Dynamic Systems;. sequential discriminative vs. generative models. Xueying. Bai, . Jiankun. Xu. Multi-label Image Classification. Co-occurrence dependency. Higher-order correlation: one label can be predicted using the previous label. Semantic redundancy: labels have overlapping meanings (cat and kitten). Zachary . C. Lipton . zlipton@cs.ucsd.edu. Time. . series. Definition. ：. A.  time series is a series of . data. . points.  indexed (or listed or graphed) in time order. . It . is a sequence of . PROYEK CIVIL . – BATCHING PLANT. TEKNOLOGI DAN MANAGEMEN. ALAT BERAT KONSTRUKSI. E-Learning PT PP (Persero), Tbk. PERALATAN-PERALATAN. PROYEK CIVIL – BATCHING PALANT. Cement Silo. Belt Conveyor. Openstack. *. Yunhong Jiang . Yunhong.Jiang@intel.com. *Other names and brands may be claimed as the property of others.. Agenda. NFV and network . l. atency. Why network latency on NFV. How to achieve . Multi-Cores . With Graph Awareness. Vijayan, Ming, Xuetian, Frank, Lidong, Maya. Microsoft Research. Motivation. Tremendous increase in graph data and applications. New class of graph applications that require real-time responses. 2. Multi-Chip-Module (MCM). A single package that includes multiple dies (chips or . . chiplets. ) – 36 . chiplets. in this case – reduces design cost. The package substrate has inter-chip links that are better. Human Language Technologies. Giuseppe Attardi. Some slides from . Arun. . Mallya. Università di Pisa. Recurrent. RNNs are called . recurrent.  because they perform the same task for every element of a sequence, with the output depending on the previous values..