A Cloud Server Oriented FPGA Accelerator for LSTM Recurrent Neural Network
نویسندگان
چکیده
منابع مشابه
A Large-Scale Spiking Neural Network Accelerator for FPGA Systems
Spiking neural networks (SNN) aim to mimic membrane potential dynamics of biological neurons. They have been used widely in neuromorphic applications and neuroscience modeling studies. We design a parallel SNN accelerator for producing large-scale cortical simulation targeting an off-theshelf Field-Programmable Gate Array (FPGA)-based system. The accelerator parallelizes synaptic processing wit...
متن کاملLearning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network
Bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) has been successfully applied in many tagging tasks. BLSTM-RNN relies on the distributed representation of words, which implies that the former can be futhermore improved through learning the latter better. In this work, we propose a novel approach to learn distributed word representations by training BLSTM-RNN on a spe...
متن کاملRecurrent neural network and LSTM models for lexical utterance classification
Utterance classification is a critical pre-processing step for many speech understanding and dialog systems. In multi-user settings, one needs to first identify if an utterance is even directed at the system, followed by another level of classification to determine the intent of the user’s input. In this work, we propose RNN and LSTM models for both these tasks. We show how both models outperfo...
متن کاملBi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation
Recurrent neural network(RNN) has been broadly applied to natural language processing(NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory(LSTM) units for Chinese word segmentation, which is a crucial preprocess ...
متن کاملA Recurrent Neural Network Model for Solving Linear Semidefinite Programming
In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and synthesis in systems and control theory. First we reformulate the problem to a linear programming problem, second we reformulate it to a first order system of ordinary differential equations. Then a recurrent neural network...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2938234