Towards an FPGA based reconfigurable computing environment for neural network implementations
Towards an FPGA based reconfigurable computing environment for neural network implementations
复制标题
面向神经网络实现的基于 FPGA 的可重构计算环境
DOI：
10.1049/cp:19991186
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
B. Gunther
中科院分区：
文献类型：
--
作者：
J. Zhu;G. Milne;B. Gunther
三个计算特性可以归因于神经网络：并行性，模块性和动态适应性。我们认为，神经网络的这些计算特性很好地映射到基于细粒度FPGA的可重构计算架构。神经网络架构被分解为一组参数化的神经计算模块，并在FPGA中实现为硬件环境。正在创建控制程序和工具来支持硬件上下文的运行时实例化，并将它们组装成神经网络，以及管理神经网络模块的动态重新配置。基于FPGA的可重构计算环境的神经网络实现的并行化的形式进行了描述。
Three computational characteristics can be attributed to neural networks: parallelism, modularity, and dynamic-adaptation. We argue that these computational characteristics of neural networks map nicely to fine-grained FPGA based reconfigurable computing architectures. Neural network architectures are decomposed into a set of parameterized neural computation modules and implemented in the FPGAs as hardware contexts. Control programs and tools are being created to support run-time instantiation of hardware contexts, and to assemble them into a neural network, as well as to manage the dynamic reconfiguration of the neural network modules. The forms of parallelism that can be exploited for neural network implementations on FPGA based reconfigurable computing environments are described.