Multiprocessor Architectures with Bio-Emulated Interconnect Topologies

Abstract

The rapid evolution of multiprocessor systems has driven the exploration of novel interconnect topologies to overcome challenges related to scalability, latency, and power consumption. Inspired by the remarkable efficiency and adaptability of biological neural networks, this paper proposes a multiprocessor architecture utilizing bio-emulated interconnect topologies that mimic key structural and functional properties of natural neural systems. The architecture leverages principles such as small-world connectivity, hierarchical modularity, and adaptive routing to achieve robust communication and efficient data transfer among processing elements. Our design integrates biologically inspired network models—incorporating features like clustered connectivity with sparse long-range links—to reduce average communication latency while maintaining high throughput and fault tolerance. The interconnect topology is dynamically reconfigurable, enabling the system to adapt to workload variations and potential faults by modifying routing paths and connection strengths, akin to synaptic plasticity in biological networks. A detailed hardware simulation of the proposed architecture was conducted using a custom multiprocessor simulator, benchmarking against conventional mesh and torus interconnects under diverse workload scenarios including synthetic traffic, graph analytics, and deep learning inference tasks. Results demonstrate that the bio-emulated topology reduces average communication latency by up to 35%, enhances bandwidth utilization by 28%, and improves fault tolerance with up to 40% fewer communication failures compared to traditional designs. Energy efficiency metrics reveal a 20% reduction in interconnect power consumption due to optimized routing and localized communication clusters. This research substantiates the viability of bio-inspired interconnect designs as a scalable, adaptive, and energy-efficient solution for next-generation multiprocessor systems. The findings suggest that embedding biologically motivated principles into hardware interconnects can significantly advance the performance and resilience of parallel computing architectures, paving the way for more intelligent and robust computing platforms.