Speech recognition HMM training on reconfigurable parallel processor

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http://doi.ieeecomputersociety.org/10.1109/FPGA.1997.624627

5th IEEE Symposium on FPGA-Based Cust ...

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	Hyun-Kyu Yun, A. Smith, H. Silverman, "Speech recognition HMM training on reconfigurable parallel processor," Field-Programmable Custom Computing Machines, Annual IEEE Symposium on, pp. 242, 5th IEEE Symposium on FPGA-Based Custom Computing Machines (FCCM '97), 1997.

	BibTex	x
	@article{ 10.1109/FPGA.1997.624627, author = {Hyun-Kyu Yun and A. Smith and H. Silverman}, title = {Speech recognition HMM training on reconfigurable parallel processor}, journal ={Field-Programmable Custom Computing Machines, Annual IEEE Symposium on}, volume = {0}, year = {1997}, issn = {1082-3409}, pages = {242}, doi = {http://doi.ieeecomputersociety.org/10.1109/FPGA.1997.624627}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

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	TY - CONF JO - Field-Programmable Custom Computing Machines, Annual IEEE Symposium on TI - Speech recognition HMM training on reconfigurable parallel processor SN - 1082-3409 SP EP A1 - Hyun-Kyu Yun, A1 - A. Smith, A1 - H. Silverman, PY - 1997 KW - parallel machines; speech recognition HMM training; reconfigurable parallel processor; Armstrong III; 20 node multi-computer; RISC processor; reconfigurable resources; FPGAs; in-circuit reprogramability; static RAM based FPGAs; computational capabilities; reconfigurable computers; substantive example application; HMM training; reconfigurable platform VL - 0 JA - Field-Programmable Custom Computing Machines, Annual IEEE Symposium on ER -

Hyun-Kyu Yun, Div. of Eng., Brown Univ., Providence, RI, USA

A. Smith, Div. of Eng., Brown Univ., Providence, RI, USA

H. Silverman, Div. of Eng., Brown Univ., Providence, RI, USA

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.

Index Terms:

parallel machines; speech recognition HMM training; reconfigurable parallel processor; Armstrong III; 20 node multi-computer; RISC processor; reconfigurable resources; FPGAs; in-circuit reprogramability; static RAM based FPGAs; computational capabilities; reconfigurable computers; substantive example application; HMM training; reconfigurable platform

Citation:

Hyun-Kyu Yun, A. Smith, H. Silverman, "Speech recognition HMM training on reconfigurable parallel processor," fccm, pp.242, 5th IEEE Symposium on FPGA-Based Custom Computing Machines (FCCM '97), 1997

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