This paper introduces a groundbreaking digital neuromorphic architecture that innovatively integrates Brain Code Unit (BCU) and Fundamental Code Unit (FCU) using mixedsignal design methodologies. Leveraging open-source datasets and the latest advances in materials science, our research focuses on enhancing the computational efficiency, accuracy, and adaptability of neuromorphic systems. The core of our approach lies in harmonizing the precision and scalability of digital systems with the robustness and energy efficiency of analog processing. Through experimentation, we demonstrate the effectiveness of our system across various metrics. The BCU achieved an accuracy of 88.0% and a power efficiency of 20.0 GOP/s/W, while the FCU recorded an accuracy of 86.5% and a power efficiency of 18.5 GOP/s/W. Our mixed-signal design approach significantly improved latency and throughput, achieving a latency as low as 0.75 ms and throughput up to 213 TOP/s. These results firmly establish the potential of our architecture in neuromorphic computing, providing a solid foundation for future developments in this domain. Our study underscores the feasibility of mixedsignal neuromorphic systems and their promise in advancing the field, particularly in applications requiring high efficiency and adaptability

翻译：本文提出了一种创新的数字神经形态架构，通过混合信号设计方法创造性地整合了脑代码单元（BCU）与基础代码单元（FCU）。借助开源数据集与材料科学的最新进展，本研究聚焦于提升神经形态系统的计算效率、准确性与适应性。其核心方法在于协调数字系统的精度与可扩展性，以及模拟处理的鲁棒性与能效优势。实验结果表明，该系统在多项指标上均展现出有效性：BCU实现了88.0%的准确率与20.0 GOP/s/W的能效，FCU则达到86.5%的准确率与18.5 GOP/s/W的能效。通过混合信号设计，系统的延迟与吞吐量显著优化，最低延迟降至0.75 ms，最高吞吐量达到213 TOP/s。这些成果充分证实了该架构在神经形态计算领域的潜力，为该领域的未来发展奠定了坚实基础。本研究强调了混合信号神经形态系统的可行性及其在推动领域进步中的价值，尤其适用于对高效率与适应性有严格要求的应用场景。