Nexus Machine: An Active Message Inspired Reconfigurable Architecture for Irregular Workloads

2025-02-17

AI Summary

Existing reconfigurable architectures achieve a balance among performance, energy efficiency, and programmability on edge devices but struggle with irregular workloads—such as sparse linear algebra and graph analytics—due to static processing element (PE) designs that cause load imbalance and inflexible control flow. This paper proposes Nexus Machine, an active message-driven reconfigurable architecture tailored for edge computing. Its core innovation lies in runtime instruction morphing and hop-by-hop instruction distribution: instructions are dynamically generated and executed on idle PEs, enabling control-flow-driven, on-chip resource self-adaptation; this is synergistically combined with sparse tensor distributed mapping and load-aware routing. Experimental results demonstrate that, under identical power and area constraints, Nexus Machine achieves 1.5× higher performance and 1.6× greater computational array utilization compared to state-of-the-art reconfigurable architectures.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Modern reconfigurable architectures are increasingly favored for resource-constrained edge devices as they balance high performance, energy efficiency, and programmability well. However, their proficiency in handling regular compute patterns constrains their effectiveness in executing irregular workloads, such as sparse linear algebra and graph analytics with unpredictable access patterns and control flow. To address this limitation, we introduce the Nexus Machine, a novel reconfigurable architecture consisting of a PE array designed to efficiently handle irregularity by distributing sparse tensors across the fabric and employing active messages that morph instructions based on dynamic control flow. As the inherent irregularity in workloads can lead to high load imbalance among different Processing Elements (PEs), Nexus Machine deploys and executes instructions en-route on idle PEs at run-time. Thus, unlike traditional reconfigurable architectures with only static instructions within each PE, Nexus Machine brings dynamic control to the idle compute units, mitigating load imbalance and enhancing overall performance. Our experiments demonstrate that Nexus Machine achieves 1.5x performance gain compared to state-of-the-art (SOTA) reconfigurable architectures, within the same power budget and area. Nexus Machine also achieves 1.6x higher fabric utilization, in contrast to SOTA architectures.

Problem

• Handles irregular workloads efficiently
• Mitigates load imbalance dynamically
• Enhances performance and fabric utilization

Innovation

• Dynamic instruction morphing via active messages
• Runtime execution on idle Processing Elements
• Enhanced fabric utilization and load balance