Title :
Speech recognition HMM training on reconfigurable parallel processor
Author :
Yun, Hyun-Kyu ; Smith, Aaron ; Silverman, Harvey
Author_Institution :
Div. of Eng., Brown Univ., Providence, RI, USA
Abstract :
Armstrong III is a 20 node multi-computer that is currently operational. In addition to a RISC processor, each node contains reconfigurable resources implemented with FPGAs. The in-circuit reprogramability of static RAM based FPGAs allows the computational capabilities of a node to be dynamically matched to the computational requirements of an application. Most reconfigurable computers in existence today rely solely on a large number of FPGAs to perform computations. In contrast, the paper demonstrates the utility of a small number of FPGAs coupled to a RISC processor with a simple interconnect. The article describes a substantive example application that performs HMM training for speech recognition with the reconfigurable platform
Keywords :
field programmable gate arrays; hidden Markov models; parallel architectures; parallel machines; reconfigurable architectures; reduced instruction set computing; speech recognition; 20 node multi-computer; Armstrong III; FPGAs; HMM training; RISC processor; computational capabilities; in-circuit reprogramability; reconfigurable computers; reconfigurable parallel processor; reconfigurable platform; reconfigurable resources; speech recognition HMM training; static RAM based FPGAs; substantive example application; Computer architecture; Data flow computing; Field programmable gate arrays; Flow graphs; Hidden Markov models; Parallel processing; Pipeline processing; Reduced instruction set computing; Signal processing algorithms; Speech recognition;
Conference_Titel :
Field-Programmable Custom Computing Machines, 1997. Proceedings., The 5th Annual IEEE Symposium on
Conference_Location :
Napa Valley, CA
Print_ISBN :
0-8186-8159-4
DOI :
10.1109/FPGA.1997.624627