To address the challenges in option pricing caused by the large sample size, latency sensitivity, and power consumption limitations of Monte Carlo simulations, this paper conducts in-depth research on four aspects: algorithms, random numbers, parallel pipelining, and resource mapping, and proposes a hardware acceleration engine framework based on FPGA. After reviewing the research progress of FPGAs in option pricing, quantitative finance, and random number generation over the past three years, the paper proposes an architecture goal for low-latency scenarios. This architecture focuses on three main aspects: geometric Brownian motion path generation, discounted return accumulation, and statistical error control, implemented using streaming data paths, path-level parallelism, on-chip random number supply, and fixed-point/floating-point co-design. The paper then presents the pricing model, discretization formula, throughput and energy efficiency evaluation indicators, and discusses the implementation methods from four aspects: module partitioning, pipelining scheduling, memory access organization, and precision control. By redrawing statistical graphs using the latest publicly available research data and conducting comparative analysis, it is shown that Versal-type AIE/PL co-design has significant advantages in throughput and energy consumption during large-scale path simulations. However, its performance ultimately depends on factors such as on-chip interconnects, random number generation bandwidth, and host-device coupling methods. Research findings indicate that the key to using FPGAs for Monte Carlo simulation of option prices lies not in parallelism, but in integrating random number generation, path movement, payout accumulation, and error control into a single pipeline using a reusable hardware engine.

Enqi Weng. Research on Monte Carlo Simulation Implementation of Option Pricing Based on FPGA Hardware Acceleration Engine for Low-Latency Derivatives Computation. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 49-58. https://doi.org/10.38007/ML.2026.060106.

[1] Dingyuan Liu. Measuring the Sensitivity of Local Skill Structures to AI Substitution Risks Based on Occupational Task Decomposition. Socio-Economic Statistics Research (2025), Vol. 6, Issue 2: 177-184

[2] Han, X. (2026). Research on Automotive Manufacturing Process Optimization Methods for Multi-Supplier Collaboration.

[3] Han, X. (2026). Research on Process Decision-Making Behavior under Incomplete Information Conditions in Automobile Manufacturing Systems.

[4] Hou, Y. (2026). Research on Server Performance Stability Assurance Mechanisms during Cross-Generation Computing Platform Upgrades.

[5] Hou, Y. (2026). Research on BIOS and BMC Compatibility Optimization Methods for Cross-Generation Servers in Production Environments.

[6] Yin, J. (2026). Research on Financial Time Series Prediction and Multiscale Correlation Based on the Fusion of Network Big Data and Deep Learning.

[7] Yu, X. (2026). Strategy Models and Practical Research of Growth Marketing under the Background of Digital Transformation.

[8] Yu, X. (2025). Digital Transformation Empowers Growth Marketing with Marketing Data Analysis Integration and Real-Time Display Strategy.

[9] Liu, H. (2026). Research on Dynamic Price Prediction of E-commerce Based on Time Series Modeling.

[10] Liu, H. (2026). Research on the Application of Causal Reasoning Method in Content Compliance Experimental Evaluation.

[11] Wang, Y. (2026). Research on the Application of Artificial Intelligence in Supply Chain Risk Early Warning.

[12] Sun, Q. (2026). Research on a Robotic Natural Language Intelligent Decision-Making Framework Based on Large Language Models, Thinking Chain Reasoning, and Multi-Agent Collaboration.

[13] Sun, Q. (2026). Research on Lightweight Intelligent Dialogue Systems Based on Semantic Entity Enhanced Intention Recognition and Rule Retrieval Generation Hybrid Models.

[14] Zhou, Y. (2024, November). Construction of a Multi-factor Quantitative Stock Selection System for the New Energy Industry Based on Microservices Architecture and Machine Learning Components. In International Conference on Cognitive based Information Processing and Applications (pp. 163-174). Singapore: Springer Nature Singapore.

[15] Huang, J. (2025, September). Performance Evaluation Index System and Engineering Best Practice of Production-Level Time Series Machine Learning System. In 2025 International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) (pp. 01-07). IEEE.

[16] Huang, J. (2025, August). Research on Multi-Model Fusion Machine Learning Demand Intelligent Forecasting System in Cloud Computing Environment. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-7). IEEE.

[17] Hua, X. (2024, November). Design and Implementation of a Game QoE Monitoring and Evaluation System Driven by Network Traffic Analysis. In International Conference on Cognitive based Information Processing and Applications (pp. 149-161). Singapore: Springer Nature Singapore.

[18] Qi, Y. (2025, October). Research on Privacy Protection of AI Models in Big Data Using Differential Privacy Technology. In 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON) (pp. 1-5). IEEE.

[19] Truong, T. H. (2026). Research on Risks and Countermeasures of Enterprise E-Commerce Transformation in the Cross-Border E-Commerce Environment.

[20] Wu, L. (2025, December). Design and Application of Automatic Data Set Generation Tool Based on KLEE in Embedded Memory Management Performance Test Framework. In 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1111-1117). IEEE.

[21] Wu, L. (2025, December). Design and Application of Automatic Data Set Generation Tool Based on KLEE in Embedded Memory Management Performance Test Framework. In 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 1111-1117). IEEE.

[22] Chen, X. (2024, November). Cloud Storage User Behavior Analysis and Dynamic Replica Strategy Optimization Based on Improved RFM and Fuzzy Clustering. In International Conference on Cognitive based Information Processing and Applications (pp. 425-434). Singapore: Springer Nature Singapore.

[23] Wang, C. (2026). Research on the Control of Uncertainty Risks in Investment Decision-making by Financial Modeling.

[24] Xinran Tu. Resource Allocation Optimization and Cost Saving Analysis Based on Data Mining. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 1: 1-9

[25] Linwei Wu. Data Visualization and Decision Support Analysis Based on Tableau. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 10-18

[26] Yilin Fu. Research on the Application of Innovative Financial Technologies in Capital Market Risk Management. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 1-9

[27] Yuhan Zhou. Green Bonds and Sustainable Financing Models in Energy Finance. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 28-35

[28] Zhengle Wei. Research on Innovative Design of Financial Derivatives and Market Risk Management Strategies. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 19-27

[29] Shuang Yuan. Research on Abnormal Detection and Transaction Risk Management Based on Machine Learning. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 10-18

[30] Lu, Z. (2025). AI-Driven Cross-Cloud Operations Language Standardisation and Knowledge Sharing System. European Journal of AI, Computing & Informatics, 1(4), 43-50.

[31] Bukun Ren. Multimodal Learning Method for Cross-Modal Data Alignment and Retrieval. International Journal of Multimedia Computing (2026), Vol. 7, Issue 1: 1-8.

[32] Yixian Jiang. Performance Optimization and Improvement of Advertising Machine Learning Platform Based on Distributed Systems. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 9-17

[33] Jin Li. Performance Analysis of Efficient Microservice Architecture in the Financial Industry. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 1-9.

[34] Huijie Pan. Discussion on Low-Latency Computing Strategies in Real-Time Hardware Generation. International Journal of Neural Network (2025), Vol. 4, Issue 1: 57-64.

[35] Lu, Z. (2025). Design and Practice of AI Intelligent Mentor System for DevOps Education. European Journal of Education Science, 1(3), 25-31.

[36] Wang, C. (2025). Research on Market Evaluation Strategies for Financial Institutions Based on Big Data Analysis.

[37] Zhang, X. (2025). Optimization and Implementation of Time Series Dimensionality Reduction Anti-fraud Model Integrating PCA and LSTM under the Federated Learning Framework. Procedia Computer Science, 262, 992-1001.

[38] Xuanrui Zhang. Exploration of the Application of Big Data Technology in Financial Fraud Monitoring. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 19-26

[39] Hong, Y. (2025). Architecture Design and Performance Optimization of a Large-scale Online Simulation Platform for Business Decision-making. Advances in Computer and Communication, 6(4).

[40] Hong, Y. (2026). Research on Warehouse Capacity Optimization Methods Based on Predictive Modeling. Engineering Advances, 6(1).

[41] Truong, T. H. (2025). Research on the Application of Digital Healthcare Platforms in Chronic Disease Management. Advances in Computer and Communication, 6(5).

[42] Xu, D. (2026). AI-Driven Video Content Optimization Strategies for Immersive Media. European Journal of Engineering and Technologies, 2(1), 1-8.

[43] Huang, J. (2026). Practice of Public Space Optimization and Functional Enhancement in Cultural Architecture. European Journal of Engineering and Technologies, 2(1), 9-21.

[44] Liu, Y. (2026). The Application of Data-Driven Financial Risk Management in Multinational Enterprises. Economics and Management Innovation, 3(1), 20-26.