Stochastic Computing for Compute-Intensive Sections of JPEG Compression


  • Minal S. Deshmukh Dr Vishwanath Karad MIT World Peace University, School of Electronics and Communication Engineering Kothrud, Pune, India
  • Prasad D. Khandekar Dr Vishwanath Karad MIT World Peace University, School of Electronics and Communication Engineering Kothrud, Pune, India
  • Ketan J. Raut Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Information and Technology, Pune, India


Stochastic Computing, Deterministic Computing, Image processing applications, FPGA


Embedded real-time image processing applications are subject to strict design constraints concerning size and power. As devices scale down to nanoscale dimensions, circuit reliability becomes an increasingly pressing concern. Stochastic computing (SC) emerges as a cost-effective alternative to conventional deterministic binary computing, achieving this by encoding and processing information through digitized probabilities. This paper delves into the exploration of reconfigurable architectures to develop precise image-processing circuits for the compute-intensive blocks within baseline JPEG compression algorithms. These encompass RGB to YCbCr conversion, Discrete Cosine Transform (DCT), and Quantization, employing both deterministic and stochastic logic. Synthesis trial results underscore that stochastic implementation requires fewer hardware resources, occupies less physical area, and consumes less power when contrasted with deterministic logic. Employing an FPGA (PYNQ-Z2), the proposed designs have been executed, leading to power reductions of 112% and 120%, as well as area utilization improvements of 14% and 67.67%, for RGB to YCbCr and DCT-Quantization stochastic circuits, respectively, in comparison with deterministic circuits


Download data is not yet available.


Armin Alaghi, Cheng Li and John P. Hayes, “Stochastic Circuits for Real-Time Image-Processing Applications”, DAC’13, May 29–June 07, 2013, Austin, TX, USA

Bouridane, D. Crookes, P. Donachy, K. Alotaibi, K. Benkrid, “A high level FPGA-based abstract machine for image processing”, Journal of Systems Architecture Volume 45, Issue 10, April 1999, Pages 809-824

Sparsh Mittal, Saket Gupta and S. Dasgupta, “FPGA: An Efficient and Promising Platform for Real-Time Image Processing Applications,” Proceedings of the National Conference on Research and Development in Hardware & Systems (CSI-RDHS), 2008

J Sarkhawas, P D Khandekar and A A Kulkarni, “Variable Quality Factor JPEG Image Compression Using Dynamic Partial Reconfiguration and Microblaze” in International Conference ICCUBEA 2015, at PCCOE Pune, pp 620-624, IEEE DOI 10.1109/ICCUBEA.2015.127.

Armin Alaghi , Weikang Qian, John P. Hayes “The Promise and Challenge of Stochastic Computing”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2017.

B. R. Gaines, Stochastic Computing Systems, Department of Electrical Engineering Science University of Essex, Colchester, Essex, U.K

Alaghi, John P. Hayes,” Survey of Stochastic Computing”, ACM Transactions on Embedded Computing Systems, Vol. 12, No. 2s, Article 92, 2013. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73

Sayed Ahmad Salehi “Low-Cost Stochastic Number Generators for Stochastic Computing”, IEEE Transactions On Very Large Scale Integration (VLSI) Systems, 2020.

Junsangsri, Pilin; Lombardi, Fabrizi, “Design of a Random Number Generator for Stochastic Computing(SC)” (2022) TechRxiv. Preprint.

P.K. Gupta; R. Kumaresan, “Binary multiplication with PN sequences” IEEE Transactions on Acoustics, Speech, and Signal Processing (Volume: 36, Issue: 4, April 1988)

Moore, Edward F., and Claude E. Shannon. "Reliable circuits using less reliable relays." Journal of the Franklin Institute 262.3 (1956): 191-208.

Von Neumann, John. “Probabilistic logics and the synthesis of reliable organisms from unreliable components.” Automata studies 34 (1956): 43-98.

Nepal, Kundan, et al. “Designing logic circuits for probabilistic computation in the presence of noise.” Proceedings of the 42nd annual Design Automation Conference. ACM, 2005

Palem, Krishna V. “Energy aware computing through probabilistic switching: A study of limits.” Computers, IEEE Transactions on 54.9 (2005): 1123 -1137.

Narayanan, Sriram, et al. “Scalable stochastic processors.” Proceedings of the Conference on Design, Automation and Test in Europe. European Design and Automation Association, 2010.

Weikang Qian; Riedel, M.D., “The synthesis of robust polynomial arithmetic with stochastic logic,” Design Automation Conference, 2008. DAC 2008. 45th ACM/IEEE, vol., no., pp.648,653, 8 -13 June 2008

Li, Peng, and David J. Lilja. “Using stochastic computing to implement digital image processing algorithms.” Computer Design (ICCD), 2011 IEEE 29th International Conference on. IEEE, 2011.

Python pro ductivity for Zynq (Pynq) Documentation, Release 2.7, Xilinx, November, 2021

Vivado Design Suite User Guide, UG910 (v2021.2) October 27, 2021.

R.C. Gonzalez and R.E. Woods, Digital Image Processing, 2nd ed., Prentice Hall, 2002

John C. Russ, F. Brent Neal, “The Image Processing Handbook, 7th Edition”, CRC Press ISBN: 9781498740289, September 2018

Rose, J. D. ., R, V. R. ., Lakshmi, D., Saranya, S. ., & Mohanaprakash, T. A. . (2023). Privacy Preserving and Time Series Analysis of Medical Dataset using Deep Feature Selection. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 51–57.

Thakre, B., Thakre, R., Timande, S., & Sarangpure, V. (2021). An Efficient Data Mining Based Automated Learning Model to Predict Heart Diseases. Machine Learning Applications in Engineering Education and Management, 1(2), 27–33. Retrieved from

Pandey, J. K., Ahamad, S., Veeraiah, V., Adil, N., Dhabliya, D., Koujalagi, A., & Gupta, A. (2023). Impact of call drop ratio over 5G network. Innovative smart materials used in wireless communication technology (pp. 201-224) doi:10.4018/978-1-6684-7000-8.ch011 Retrieved from




How to Cite

Deshmukh, M. S. ., Khandekar, P. D. ., & Raut, K. J. . (2023). Stochastic Computing for Compute-Intensive Sections of JPEG Compression. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 51–59. Retrieved from



Research Article