Multiplier supporting accuracy and energy trade-offs for recognition applications

Multiplier supporting accuracy and energy trade-offs for recognition applications

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The need to support various recognition applications on energy-constrained mobile computing devices has steadily grown. Exploiting common characteristics of recognition algorithms, a very energy-efficient multiplier that can support a runtime trade-off between computational accuracy and energy consumption is proposed. Compared to a precise multiplier, the proposed multiplier consumes 11.6×–3.2× less energy per multiplication while achieving 82–99% of the computational accuracy with negligible negative impact on recognition accuracy for three different recognition applications.


    1. 1)
      • 1. Hegde, R., Shanbhag, N.: ‘Energy-efficient signal processing via algorithmic noise-tolerance’. Int. Symp. Low Power Electronics and Design, San Diego, CA, USA, August 1999.
    2. 2)
      • 2. Kulkarni, P., Gupta, P., Ercegovac, M.: ‘Trading accuracy for power with an underdesigned multiplier architecture’. Int. Conf. VLSI Design, Chennai, India, January 2011.
    3. 3)
    4. 4)
      • 4. LeCun, Y., Cortes, C., Burges, C.: ‘The MNIST database of handwritten digits accessed August 2012.
    5. 5)
      • 5. Reservoir Lab: ‘Reservoir computing accessed December 2012.
    6. 6)
      • 6. Liberman, M., Amsler, R., Church, K., et al: ‘TI 46 words speech database accessed October 2010.
    7. 7)
      • 7. Sakhi, O.: ‘Face detection using support vector machine (SVM) accessed October 2012.

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