Design and Validation of a Smart Neuromorphic System Architecture for Algorithmic Trading
Shuming Xu, Lu Jiang, Boping Gu
Abstract
This study proposes a neuromorphic computing architecture specifically designed for high-frequency financial trading, addressing the limitations of traditional computing platforms in ultra-low latency, energy efficiency, and system reliability. The designed heterogeneous neuromorphic system integrates Intel Loihi 2 and IBM TrueNorth chips, enabling ultra-fast market data processing and decision-making through spiking neural networks. The system architecture comprises a front-end data acquisition layer, a neuromorphic processing layer, and a decision execution layer, employing an event-driven computing paradigm to significantly reduce power consumption. The computational acceleration framework developed in this study is optimized for finance-specific algorithms, including graph convolutional networks for market microstructure analysis and hybrid LSTM-CNN models for liquidity assessment. The proposed fault-tolerant mechanism ensures continuous operation during partial hardware failures through redundant design and dynamic task redistribution. The system integrates real-time audit trail functionality compliant with regulatory requirements and features dedicated interfaces for compatibility with existing trading infrastructure. This system delivers a low-latency, energy-efficient, and highly reliable computational platform for high-frequency trading, offering a novel technical pathway for building intelligent infrastructure in financial markets.