TR
EN
On the Limit of Multiplexers in Stochastic Computing
Abstract
Stochastic computing (SC) is an approach used in today's re-emerging hardware environments. Known deterministic circuit elements are fed by binary sequences with probability, and the output sequence probability expresses a mathematical operation in terms of the probability of input sequences. Pulse trains expressed with probability values feed deterministic logic systems by expressing unipolar or bipolar encoding techniques, and an output pulse train with a probability value is obtained. This approach, which provides benefits in terms of complexity, low power, and durability especially for arithmetic operations, appears in applications with flexible fault tolerance such as computer vision. In this context, the multiplexer (MUX) logic system is used as a scaled adder; in other words, the sum of binary probabilistic sequences coming to the inputs of a MUX is seen at the output at the rate of a coefficient. In this study, the limits of the MUX structure within the scope of SC are underlined. With the MUX structures created with different hardware configurations, the architectures are investigated for performance.
Keywords
Supporting Institution
Istanbul Technical University - BAP
Project Number
MDK-2018-41532
Thanks
This work is supported by the Istanbul Technical University, BAP, with the project ID MDK-2018-41532.
References
- [1] H. Sim and J. Lee, "A new stochastic computing multiplier with application to deep convolutional neural networks," Proc. 54th Annu. Des. Autom. Conf. 2017 - DAC '17, pp. 1–6, 2017, doi: 10.1145/3061639.3062290.
- [2] H. Sim and J. Lee, "Cost-effective stochastic MAC circuits for deep neural networks," Neural Networks, vol. 117, pp. 152–162, 2019, doi: 10.1016/j.neunet.2019.04.017.
- [3] B. Li, M. H. Najafi, B. Yuan, and D. J. Lilja, "Quantized neural networks with new stochastic multipliers," Proc. - Int. Symp. Qual. Electron. Des. ISQED, pp. 376–382, 2018, doi: 10.1109/ISQED.2018.8357316.
- [4] D. J. Lilja, "Low-cost stochastic hybrid multiplier for quantized," J. Emerg. Technol. Comput. Syst. vol. 15(2), 2019. doi: 10.1145/3309882.
- [5] A. Ardakani, et al., "VLSI implementation of deep neural network using integral stochastic computing," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 25, no. 10, pp. 2688-2699, Oct. 2017, doi: 10.1109/TVLSI.2017.2654298.
- [6] V. T. Lee, et al., "Energy-efficient hybrid stochastic-binary neural networks for near-sensor computing," 2017 Des. Autom. Test Eur. DATE, pp. 13–18, 2017, doi: 10.23919/DATE.2017.7926951.
- [7] S. Aygun, E. O. Gunes, and C. De Vleeschouwer, "Efficient and robust bitstream processing in binarised neural networks," Electron. Lett., vol. 57, no. 5, pp. 219–222, 2021, doi: 10.1049/ell2.12045.
- [8] I. Perez-Andrade, et al., "Stochastic computing improves the timing-error tolerance and latency of turbo decoders : Design guidelines and tradeoffs," IEEE Access, vol. 4, pp. 1008-1038, 2016, doi: 10.1109/ACCESS.2016.2523063.
Details
Primary Language
English
Subjects
Engineering
Journal Section
Research Article
Publication Date
July 31, 2021
Submission Date
June 29, 2021
Acceptance Date
July 1, 2021
Published in Issue
Year 2021 Volume: 5 Number: 1
APA
Aygün, S., & Güneş, E. O. (2021). On the Limit of Multiplexers in Stochastic Computing. International Journal of Multidisciplinary Studies and Innovative Technologies, 5(1), 94-97. https://izlik.org/JA66ZZ38SF
AMA
1.Aygün S, Güneş EO. On the Limit of Multiplexers in Stochastic Computing. IJMSIT. 2021;5(1):94-97. https://izlik.org/JA66ZZ38SF
Chicago
Aygün, Sercan, and Ece Olcay Güneş. 2021. “On the Limit of Multiplexers in Stochastic Computing”. International Journal of Multidisciplinary Studies and Innovative Technologies 5 (1): 94-97. https://izlik.org/JA66ZZ38SF.
EndNote
Aygün S, Güneş EO (July 1, 2021) On the Limit of Multiplexers in Stochastic Computing. International Journal of Multidisciplinary Studies and Innovative Technologies 5 1 94–97.
IEEE
[1]S. Aygün and E. O. Güneş, “On the Limit of Multiplexers in Stochastic Computing”, IJMSIT, vol. 5, no. 1, pp. 94–97, July 2021, [Online]. Available: https://izlik.org/JA66ZZ38SF
ISNAD
Aygün, Sercan - Güneş, Ece Olcay. “On the Limit of Multiplexers in Stochastic Computing”. International Journal of Multidisciplinary Studies and Innovative Technologies 5/1 (July 1, 2021): 94-97. https://izlik.org/JA66ZZ38SF.
JAMA
1.Aygün S, Güneş EO. On the Limit of Multiplexers in Stochastic Computing. IJMSIT. 2021;5:94–97.
MLA
Aygün, Sercan, and Ece Olcay Güneş. “On the Limit of Multiplexers in Stochastic Computing”. International Journal of Multidisciplinary Studies and Innovative Technologies, vol. 5, no. 1, July 2021, pp. 94-97, https://izlik.org/JA66ZZ38SF.
Vancouver
1.Sercan Aygün, Ece Olcay Güneş. On the Limit of Multiplexers in Stochastic Computing. IJMSIT [Internet]. 2021 Jul. 1;5(1):94-7. Available from: https://izlik.org/JA66ZZ38SF