Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Units

TL;DR

The paper tackles the challenge of building energy-efficient, high-accuracy neuromorphic hardware by integrating Brain Code Units (BCUs) and Fundamental Code Units (FCUs) within a mixed-signal digital architecture. It develops data handling and preprocessing pipelines (BrainMRI and CIFAR-10), implements BCU/SNN-based MRI classification and FCU-based arithmetic-logic tasks on GPUs and FPGA platforms, and validates the design through a full RTL-to-GDSII flow using OpenLane. Key results show BCUs achieving 88.0% accuracy with 20.0 GOP/s/W and FCUs achieving 86.5% accuracy with 18.5 GOP/s/W, while the mixed-signal design delivers notably low latency (as low as 0.75 ms) and high throughput (up to 213 TOP/s), outperforming traditional digital CMOS in latency and energy efficiency. These findings demonstrate the feasibility and practical potential of mixed-signal neuromorphic systems for high-efficiency, adaptable computing workloads, and they provide a concrete hardware pathway toward brain-inspired accelerators. The work highlights the importance of integrating analog robustness with digital precision to realize real-time, power-conscious neuromorphic processors for applications in medical imaging, edge AI, and sensor-rich environments.

Abstract

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

Figures (5)

• Figure 1: Differential Encoding of Abstract Mathematical Rules in Human. This figure illustrates the contrast between dynamic coding in the parahippocampal cortex and static coding in the hippocampus. The top panel depicts the sequence of abstract operations over time, linked to distinct neural activities. On the bottom left, dynamic coding is visualized as shifting activity patterns corresponding to different abstract rules. Conversely, the bottom right heatmap demonstrates static coding with consistent activation regions irrespective of changes in abstract rules. This indicates a division of labor within the human medial temporal lobe in the processing of abstract information, with implications for understanding the neural basis of high-level cognition.
• Figure 2: Overview of Brain-like Computing Paradigm.
• Figure 3: Integration of Fundamental Code Unit (FCU) and Brain Code Unit (BCU) in Neuromorphic Systems.
• Figure 4: Block Diagram of Implementation
• Figure 5: Performance of our framework across three distinct hardware platforms using various datasets.