Scenario-Aware Control of Segmented Ladder Bus: Design and FPGA Implementation

2025-11-19

AI Summary

Existing dynamic segmented buses lack lightweight, scalable, and scenario-adaptive control mechanisms to address the sparsity, asynchrony, and locality inherent in spike communication within large-scale neuromorphic architectures. This work proposes a scenario-aware control plane for segmented trapezoidal buses, which dynamically reconfigures bus segmentation based on real-time traffic characteristics—enabling hardware-minimal and demand-driven activation of control logic. The design reduces control overhead significantly (control area is only 12.3% of the data plane) while preserving O(1) area and power scalability, achieving a 2.8× improvement in energy efficiency. FPGA prototyping and cycle-accurate simulation validate sub-microsecond arbitration latency and >94% bandwidth utilization at scale—up to 1,000 nodes.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Large-scale neuromorphic architectures consist of computing tiles that communicate spikes using a shared interconnect. The communication patterns in these systems are inherently sparse, asynchronous, and localized, as neural activity is characterized by temporal sparsity with occasional bursts of high traffic. These characteristics require optimized interconnects to handle high-activity bursts while consuming minimal power during idle periods. Among the proposed interconnect solutions, the dynamic segmented bus has gained attention due to its structural simplicity, scalability, and energy efficiency. Since the benefits of a dynamic segmented bus stem from its simplicity, it is essential to develop a streamlined control plane that can scale efficiently with the network. In this paper, we present a design methodology for a scenario-aware control plane tailored to a segmented ladder bus, with the aim of minimizing control overhead and optimizing energy and area utilization. We evaluated our approach using a combination of FPGA implementation and software simulation to assess scalability. The results demonstrated that our design process effectively reduces the control plane's area footprint compared to the data plane while maintaining scalability with network size.

Problem

• Designing scalable control plane for neuromorphic segmented bus interconnect
• Minimizing control overhead while optimizing energy and area utilization
• Handling sparse asynchronous neural communication with efficient burst management

Innovation

• Scenario-aware control plane for segmented ladder bus
• Minimizes control overhead and optimizes energy usage
• FPGA implementation demonstrates reduced area footprint