Titlebook: Algorithms and Architectures for Parallel Processing; 20th International C Meikang Qiu Conference proceedings 2020 Springer Nature Switzerl

Terminal

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/153061.jpg

BUOY

Distributing Data in Real Time Spatial Data Warehouseocessing becomes more and more important. As a result, there is a tremendous amount of real-time spatial data in real-time spatial data warehouse. The continuous growth in the amount of data seems to outspeed the advance of the traditional centralized real-time spatial data warehouse. As a solution,

彈藥

Accelerating Sparse Convolutional Neural Networks Based on Dataflow Architectureion, and natural language processing. Large-scale CNNs generally have encountered limitations in computing and storage resources, but sparse CNNs have emerged as an effective solution to reduce the amount of computation and memory required. Though existing neural networks accelerators are able to ef

工作

DAFEE: A Scalable Distributed Automatic Feature Engineering Algorithm for Relational Datasetsrity of existing approaches are designed to handle tasks with only one data source, which are less applicable to real scenarios. In this paper, we present a distributed automatic feature engineering algorithm, DAFEE, to generate features among multiple large-scale relational datasets. Starting from

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CAND

Design of a Convolutional Neural Network Instruction Set Based on RISC-V and Its Microarchitecture Ierence processes. Various software optimization has been examined towards existing hardware devices such as CPU and GPU to meet the computation needs; however, the performance gap between ideal and reality will keep going if there is short of hardware support. In this paper, we propose a customized

Mri485

Optimizing Accelerator on FPGA for Deep Convolutional Neural Networksional neural networks (CNNs) has been widely concerned because of its high precision advantage. However, CNNs are usually computationally large. And in addition to the widely used GPUs, but which has higher energy. And FPGA is gradually used to achieve CNNs acceleration due to its high performance,

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