Parallel Architectures for Speech Recognition: Nonlinear Optimization of Expectation-Maximization (EM) Training of Hidden Markov Models (HMMs) in a Reconfigurable Environment

这项研究涉及高速计算新体系结构的开发、构建和测试。正在开发的想法是以松散耦合的方式组合基于RISC的通用处理节点，每个处理节点都具有多个基于现场可编程逻辑阵列(FPLA)的协处理器系统。利用这种系统，人们具有通用计算机的硬件特性，但由适当的可重构性带来的优势是性能更类似于专用发动机的性能。特别是，20个节点的阿姆斯特朗III系统已经建成并投入使用，比目前先进的工作站改进了近两个数量级。硬件系统及其配置编译器是此类体系结构的基本构建块，该程序可将C函数子例程自动转换为机器码和可重构协处理器的硬件设计。因此，这项研究直接集中在国家关注的战略领域之一，即高性能计算。这一计划的独特性在于，不仅硬件/软件系统已经建立，而且它还在重要的真实应用中进行测试。特别是，基于现代隐马尔可夫模型(HMM)的语音识别系统的训练需要数百个小时，即使是最近在Brown和其他地方开发的一些变体也是如此。将训练时间减少到10分钟左右意味着这一领域的研究进展可以以更快的速度进行，和/或现在可以将昂贵的非线性优化技术应用于该问题。此外，这意味着来自不太笨重的传感器系统(布朗大学正在开发的麦克风阵列系统)的数据可以适当地合并到更健壮的语音识别系统中。目前，有8名博士生、2名SCM学生和5名本科生参与了这个项目；Ni Ne博士已经在过去的资助下从这个项目毕业。

This research involves the development, construction and testing of new architectures for high-speed computing. The idea being exploited is that of combining in a loosely-coupled fashion, general-purpose, RISC-based processing nodes each with multiple field-programmable-logic-array(FPLA) based coprocessor systems. With this kind of system, one has the hardware properties of a general-purpose computer, but the advantage, brought about by suitable reconfigurability, of performance more akin to that of a special-purpose engine. In particular, the 20-node Armstrong III system has been built and is operational and provides nearly two-orders of magnitude improvement over current advanced workstations. The hardware system, combined with its configuration compiler, a program that automatically translates a C function subroutine to both machine code and to the hardware design of the reconfigurable coprocessors, are the basic building blocks for this class of architectures. Thus, this research is directly focused on one of the strategic areas of national concern, that of high performance computing. There is uniqueness to this program in that not only has the hardware/software system been built, but it is also being tested on important, real applications. In particular, the training of a modern hidden Markov model(HMM) based speech recognition system requires hundreds of hours, even with some of the variants developed recently at Brown and elsewhere. The reduction of this training time to ten or so minutes means that research progress in this area may be made at a faster rate and/or that expensive nonlinear optimization techniques may now be applied to the problem. Also, this means that data from a less cumbersome sensor system (the microphone array systems in place and being developed at Brown) can be suitably incorporated into a more robust speech recognition system. Currently, eight PhD students, two ScM students and five undergraduates are involved with this project; ni ne PhD's have been graduated from this project under past funding.