Computational Cost Reduction in Learned Transform Classifications

نویسندگان

  • Emerson Lopes Machado
  • Cristiano Jacques Miosso
  • Ricardo von Borries
  • Murilo Coutinho
  • Pedro de Azevedo Berger
  • Thiago Marques
  • Ricardo Pezzuol Jacobi
چکیده

We present a theoretical analysis and empirical evaluations of a novel set of techniques for computational cost reduction of classifiers that are based on learned transform and soft-threshold. By modifying optimization procedures for dictionary and classifier training, as well as the resulting dictionary elements, our techniques allow to reduce the bit precision and to replace each floating-point multiplication by a single integer bit shift. We also show how the optimization algorithms in some dictionary training methods can be modified to penalize higher-energy dictionaries. We applied our techniques with the classifier Learning Algorithm for Soft-Thresholding, testing on the datasets used in its original paper. Our results indicate it is feasible to use solely sums and bit shifts of integers to classify at test time with a limited reduction of the classification accuracy. These low power operations are a valuable trade off in FPGA implementations as they increase the classification throughput while decrease both energy consumption and manufacturing cost. Moreover, our techniques reduced 50% of the bit precision in almost all datasets we tested, enabling to use 32-bit operations instead of 64-bit in GPUs, which can almost double the classification throughput. © 2015 Elsevier Ltd. All rights reserved.

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عنوان ژورنال:
  • CoRR

دوره abs/1504.06779  شماره 

صفحات  -

تاریخ انتشار 2015