Overview of Stochastic Computing Applications and Challenges

CHEN Lu; WANG Jiang Yuan; ZHONG Kuncai; ZHANG Ji Liang

doi:10.11999/JEIT250413

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CHEN Lu, WANG Jiang Yuan, ZHONG Kuncai, ZHANG Ji Liang. Overview of Stochastic Computing Applications and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250413

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CHEN Lu, WANG Jiang Yuan, ZHONG Kuncai, ZHANG Ji Liang. Overview of Stochastic Computing Applications and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250413

CHEN Lu, WANG Jiang Yuan, ZHONG Kuncai, ZHANG Ji Liang. Overview of Stochastic Computing Applications and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250413

Citation:

CHEN Lu, WANG Jiang Yuan, ZHONG Kuncai, ZHANG Ji Liang. Overview of Stochastic Computing Applications and Challenges[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250413

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Overview of Stochastic Computing Applications and Challenges

doi: 10.11999/JEIT250413 cstr: 32379.14.JEIT250413

Hunan University, Changsha 410082, China

Funds: The National Natural Science Foundation of China (62122023), The Natural Science Foundation of Hunan Province (2025JJ60433), The Natural Science Foundation of Changsha City (kq2402003)

Received Date: 2025-05-13
Rev Recd Date: 2025-09-15

Available Online: 2025-09-19

Abstract

Abstract

Significance This paper systematically organizes and analyzes the historical progress, fundamental characteristics, application scenarios, and challenges of Stochastic Computing (SC), making four main contributions. (1) Integration of theoretical frameworks and refinement of knowledge systems. By reviewing the evolution of SC from its theoretical origins in the 1940s to its resurgence in the 21st-century Internet of Things era, the paper establishes a coherent theoretical trajectory. The analysis of unipolar and bipolar encoding mechanisms, stochastic bitstream generation architectures, and computational error models provides researchers with a unified technical framework. (2) Demonstration of application potential. Through examinations of three representative scenarios, namely digital filters, image processing, and neural networks, the paper highlights SC’s advantages in hardware efficiency and fault tolerance. For instance, XNOR-gate and multiplexer-based digital filter designs reduce hardware resource consumption by several orders of magnitude, whereas neural network acceleration schemes that employ low-discrepancy sequence-based stochastic sources markedly improve energy efficiency in edge AI devices. These case studies provide implementable technical pathways for engineering practice. (3) Identification of critical challenges and evaluation of solutions. Addressing three major challenges, including correlation accumulation, excessive hardware overhead in random number generation, and the precision–efficiency trade-off, the paper not only quantifies their technical origins but also evaluates the effectiveness and limitations of existing solutions, offering clear optimization directions for further research. (4) Strategic guidance for future research. By integrating emerging technological trends, the paper proposes directions such as algorithm–hardware co-design, dynamic correlation suppression, and adaptive precision adjustment. Special emphasis is placed on the potential of reconfigurable methods and novel architectures to overcome current bottlenecks, outlining research frontiers for both academia and industry. Conclusions This paper systematically reviews the historical development and foundational principles of SC, elaborates on representative application scenarios, and examines the core technical challenges it currently faces. Compared with traditional deterministic numerical computation, SC offers advantages including low hardware overhead, high asymptotic precision, and strong fault tolerance, which have enabled its adoption in digital signal processing, neural network acceleration, and edge computing. Nevertheless, several critical challenges persist and must be resolved to advance its practical deployment. Prospects As a promising pathway to address the computing power and energy efficiency challenges of the post-Moore era, the future development of SC will emphasize overcoming technical bottlenecks and adapting to emerging application scenarios. Advances in reconfigurable computing architectures, memristor-based memory devices, and compute-in-memory chips provide new opportunities for architectural innovation and performance optimization of SC systems. These developments further enhance its intrinsic advantages of low power consumption, high fault tolerance, and progressive precision, positioning SC as a key technological foundation for building high-efficiency computing systems in the post-Moore era.
- Stochastic Computing (SC),
- Probabilistic encoding,
- Low hardware complexity,
- Correlation accumulation

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