深度学习算法可重构加速器关键技术研究

项目名称： 深度学习算法可重构加速器关键技术研究

项目编号： No.61303070

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 姜晶菲

作者单位： 中国人民解放军国防科学技术大学

项目金额： 23万元

中文摘要： 深度学习算法已成为机器学习领域最新最重要的一类人工智能算法，应用范围和数据规模日益增大。由于涉及多次迭代和大规模矩阵运算，该类算法处理成为典型的计算密集型过程，其加速技术渐成为近年的研究热点。FPGA是实现高性能深度学习算法的有效平台，而目前的研究仅限于对深度信任网络这一种特定算法的定制实现，不能满足该类算法集合中各种改进和变形流程的加速需求，适用性差，算法精度影响分析不足。本项目将针对主流深度学习算法集合，研究算法流程共性和特异性,归纳统一的算法模板，建立定点化精度影响分析模型；面向FPGA特征，提出可重构的深度学习算法异构加速器体系结构，重点研究一套有效的硬件实现和加速优化方法；研究加速器参数化设计方法和自动生成技术，实现一个面向深度学习算法典型应用的可重构FPGA加速器原型系统和一个加速器自动生成软件系统，满足算法精度要求，全面提高这类算法加速应用的性能和灵活性，应用前景广阔。

中文关键词： 深度学习；FPGA；加速器；定点化；自动生成

英文摘要： Deep Learning algorithms are state-of-art Machine Learning techniques and the most important category of contemporary AI algorithm. The scope of applications and the scale of data using these algorithms are all growing fast. Deep Learning algorithms are computationally intensive due to its processing characteristics of multiple iterations and large scale matrix manipulation, which naturally leads to investigate acceleration technology as a "hot topic" in recent research committee. FPGA is one of the most suitable platforms to implement high-performance Deep Learning algorithms. Previous studies have focused only on customized accelerators of Deep Belief Networks which are lack of flexibility and analysis on accuracy implications. Reconfigurable accelerator architecture for the mainstraem Deep Learning algorithms will be studied in this project. The extraction technology of common procedures and the special operations in those algorithms will be investigated and an uniformed algorithm template will be induced. An analysis model of accuracy will be built to evaluate the effect of fixed-point representation. A kind of asynchronous architecture for Deep Learning algorithms accelerator based on FPGA will be proposed, among which several optimization methods in hardware design will be deeply studied. The design method

英文关键词： Deep learning；FPGA；Accelerator；Fixed-Point；Code Generation