Implementation of machine learning applications on a fixed-point DSP

In this paper, we discuss efficient implementation of machine learning algorithms on DSPs. Specifically, we implement OCR and speech recognition on DSP and show how they can be optimized using fixed point routines. We illustrate the optimal usage of DSP resources like MAC units, shifters and softwar...

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Bibliographic Details
Main Authors: Bharati, K. Swetha, Jhunjhunwala, Ashok
Format: Conference Proceeding
Language:English
Subjects:
Digital signal processing
Feature extraction
Hidden Markov models
Mel frequency cepstral coefficient
Optical character recognition software
Speech
Speech recognition
Online Access:Request full text
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Summary:In this paper, we discuss efficient implementation of machine learning algorithms on DSPs. Specifically, we implement OCR and speech recognition on DSP and show how they can be optimized using fixed point routines. We illustrate the optimal usage of DSP resources like MAC units, shifters and software pipelining through assembly code structuring which massively reduces the MIPS consumed by the processor. We also describe how floating point overheads can be reduced by equivalent fixed point routines for real time implementations. Though the Blackfin-533 DSP is chosen for this illustration, the ideas presented here apply to other fixed point DSPs as well.
ISSN:0840-7789
2576-7046
DOI:10.1109/CCECE.2015.7129495