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Energy-Efficient Approximate Multiplication for Digital Signal Processing and Classification Applications
Abstract
The need to support various digital signal processing (DSP) and classification applications on energy-constrained devices has steadily grown. Such applications often extensively perform matrix multiplications using fixed-point arithmetic while exhibiting tolerance for some computational errors. Hence, improving the energy efficiency of multiplications is critical. In this brief, we propose multiplier architectures that can tradeoff computational accuracy with energy consumption at design time. Compared with a precise multiplier, the proposed multiplier can consume 58% less energy/op with average computational error of $\sim 1$ %. Finally, we demonstrate that such a small computational error does not notably impact the quality of DSP and the accuracy of classification applications.
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Adaptive noise cancelling: Principles and applications
Widrow, B., Glover, J. R., McCool, J. M., Kaunitz, J., Williams, C. S., Hearn, R. H., Zeidler, J. R., Eugene Dong, Jr., & Goodlin, R. C. (1975) · Proceedings of the IEEE · doi:10.1109/proc.1975.10036 · Cited by 3,939
Cited by
Dual-Quality 4:2 Compressors for Utilizing in Dynamic Accuracy Configurable Multipliers
Akbari, O., Kamal, M., Afzali-Kusha, A., & Pedram, M. (2017) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2016.2643003 · Cited by 312
Metadata
- Authors
- Narayanamoorthy, S., Moghaddam, H. A., Liu, Z., Park, T., & Kim, N. S.
- Venue
- IEEE Transactions on Very Large Scale Integration (VLSI) Systems
- Published
- 2015-06
- Volume
- 23
- Issue
- 6
- Pages
- 1180-1184
- ISSN
- 1063-8210 (ISSN-L)
- 1557-9999
- OpenAlex
- W2065359960
- Cited by
- 229
- References
- 2
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Narayanamoorthy, S., Moghaddam, H. A., Liu, Z., Park, T., & Kim, N. S. (2015). Energy-Efficient Approximate Multiplication for Digital Signal Processing and Classification Applications. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. https://doi.org/10.1109/tvlsi.2014.2333366
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