随机计算应用与挑战概述

陈璐; 王强源; 钟坤材; 张吉良

doi:10.11999/JEIT250413

随机计算应用与挑战概述

doi: 10.11999/JEIT250413 cstr: 32379.14.JEIT250413

湖南大学长沙 410082

基金项目: 国家自然科学基金(62122023)，湖南省自然科学基金(2025JJ60433)，长沙市自然科学基金(kq2402003)

详细信息

作者简介:
陈璐：女，硕士，研究方向为新型计算电路计算机辅助设计

王强源：男，博士，研究方向为新型计算电路计算机辅助设计

钟坤材：男，助理教授，研究方向为新型计算电路计算机辅助设计，高效安全可信根设计

张吉良：男，教授，研究方向为集成电路设计与EDA相关领域

通讯作者:
张吉良　zhangjiliang@hnu.edu.cn

中图分类号: TN43
计量
- 文章访问数: 25
- HTML全文浏览量: 9
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2025-05-13
- 修回日期: 2025-09-15
- 网络出版日期: 2025-09-19

Overview of Stochastic Computing Applications and Challenges

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)

摘要

摘要: 随机计算是一种以概率信号替代确定性二进制数值的新型计算范式，其核心在于将确定性数值映射为概率化比特数流，通过统计特性而非精确位权实现算术运算。相较于传统确定性数值计算，随机计算具有低硬件开销、高渐进精度与高容错性等优势，广泛应用于数字信号处理、神经网络加速及边缘计算。然而，该技术的发展面临三大关键挑战：序列长度制约的精度与效率权衡、概率转换电路的开销过高以及随机比特流相关性导致的误差累积。该文系统梳理了随机计算的发展脉络与基本原理，重点聚焦其在低功耗滤波、实时图像处理及容错神经网络中的典型应用与实现机制。同时，深入剖析了应对上述挑战的研究策略，包括随机比特流相关性的度量、抑制与反用技术，概率转换电路硬件开销的优化策略，以及动态渐进精度调节机制的最新进展与局限。该文旨在为研究者清晰呈现随机计算的技术现状、应用潜力及未来突破方向。
- 随机计算 /
- 概率编码 /
- 低硬件复杂度 /
- 相关性积累
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

HTML全文

图 1 随机计算架构

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图 2 相关性干扰

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图 3 随机乘法器与随机加法器

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图 4 1阶随机计算数字滤波器

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图 5 神经网络架构

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图 6 去相关化技术

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图 7 随机数生成器优化技术

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表 1 单极与双极格式

格式数值数值范围与单极值Px的关系

单极 $ {N}^{1}/N $ [0,1] $ Px $

双极 ($ {N}^{1}-{N}_{0} $)$ /N $ [–1,1] $ 2\mathrm{P}\mathrm{x}-1 $

下载: 导出CSV

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