Self-Evolving Distributed Memory Architecture for Scalable AI Systems

2026-01-09

arXiv.org

AI Summary

This work addresses the limited scalability and low resource utilization in distributed AI systems caused by fragmented memory management across computation, communication, and deployment layers. To overcome this, the authors propose a three-tier self-evolving distributed memory architecture that enables the first cross-layer cooperative memory management. The system features dynamic matrix partitioning, network-aware peer-to-peer routing, runtime continuous reconfiguration, and a dual-memory mechanism that jointly optimizes long-term performance trends and short-term load awareness for workload-adaptive resource allocation. Evaluated on COCO 2017, ImageNet, and SQuAD benchmarks, the approach achieves 87.3% memory utilization and 142.5 OPS throughput—improving upon Ray by 21.1% and 44.4%, respectively—while reducing communication latency by 30.2% to 171.2 ms and attaining an overall resource utilization of 82.7%.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Distributed AI systems face critical memory management challenges across computation, communication, and deployment layers. RRAM based in memory computing suffers from scalability limitations due to device non idealities and fixed array sizes. Decentralized AI frameworks struggle with memory efficiency across NAT constrained networks due to static routing that ignores computational load. Multi agent deployment systems tightly couple application logic with execution environments, preventing adaptive memory optimization. These challenges stem from a fundamental lack of coordinated memory management across architectural layers. We introduce Self Evolving Distributed Memory Architecture for Scalable AI Systems, a three layer framework that unifies memory management across computation, communication, and deployment. Our approach features (1) memory guided matrix processing with dynamic partitioning based on device characteristics, (2) memory aware peer selection considering network topology and computational capacity, and (3) runtime adaptive deployment optimization through continuous reconfiguration. The framework maintains dual memory systems tracking both long term performance patterns and short term workload statistics. Experiments on COCO 2017, ImageNet, and SQuAD show that our method achieves 87.3 percent memory utilization efficiency and 142.5 operations per second compared to Ray Distributed at 72.1 percent and 98.7 operations per second, while reducing communication latency by 30.2 percent to 171.2 milliseconds and improving resource utilization to 82.7 percent. Our contributions include coordinated memory management across three architectural layers, workload adaptive resource allocation, and a dual memory architecture enabling dynamic system optimization.

Problem

• Distributed AI
• Memory Management
• Scalability
• Resource Allocation
• System Optimization

Innovation

• Self-Evolving Memory
• Distributed AI
• Memory-Aware Scheduling
• Dynamic Partitioning
• Dual Memory Architecture