[Paper] Self-Evolving Distributed Memory Architecture for Scalable AI Systems

Source: arXiv - 2601.05569v1

Overview

The paper proposes a Self‑Evolving Distributed Memory Architecture (SEDMA) that ties together memory handling across three layers of a large‑scale AI system: the compute kernels, the networking fabric, and the deployment/runtime environment. By letting each layer share a “dual‑memory” view of long‑term performance trends and short‑term workload spikes, the system can continuously re‑partition data, pick better peers, and redeploy services on‑the‑fly—leading to markedly higher memory utilization and lower latency than existing distributed AI stacks such as Ray.

Key Contributions

• Three‑layer unified memory management that coordinates compute, communication, and deployment resources.
• Memory‑guided matrix processing: dynamic tensor partitioning that adapts to the physical characteristics (e.g., RRAM non‑idealities, array size) of each compute node.
• Memory‑aware peer selection: routing decisions that factor in network topology, NAT constraints, and each node’s current memory pressure.
• Runtime adaptive deployment: continuous re‑configuration of containers/VMs based on short‑term workload statistics, decoupling application logic from the execution environment.
• Dual‑memory architecture: a long‑term performance repository + a short‑term workload cache that together drive autonomous optimization.
• Empirical validation on vision (COCO‑2017, ImageNet) and NLP (SQuAD) workloads, showing up to 15 % higher memory efficiency and 30 % lower communication latency versus a leading distributed framework.

Methodology

1. Layered Abstraction

   • Computation Layer: Each worker hosts a memory‑guided matrix processor that slices tensors according to the node’s memory bandwidth and RRAM device profile.
   • Communication Layer: A memory‑aware peer selector builds a weighted graph of available peers, where edge weights combine network RTT, NAT traversal cost, and each peer’s current memory load.
   • Deployment Layer: A runtime optimizer monitors short‑term statistics (e.g., incoming request burst, cache hit‑rate) and triggers container migration or scaling actions without stopping the overall job.

2. Dual‑Memory System

   • Long‑Term Memory (LTM): Persistent logs of historical performance (e.g., per‑device error rates, average utilization) that inform the baseline partitioning strategy.
   • Short‑Term Memory (STM): In‑memory counters refreshed every few seconds, capturing the current workload shape and network congestion.

3. Self‑Evolving Loop

   • Collect → Analyze → Adapt: The system continuously gathers STM data, compares it against LTM trends, and decides whether to repartition matrices, reroute messages, or redeploy services.
   • Feedback: Every adaptation is logged back into LTM, allowing the architecture to “learn” optimal configurations over time.

4. Experimental Setup

   • Benchmarks run on a heterogeneous cluster (CPU, GPU, and emerging RRAM‑based accelerators) connected via a mix of LAN and NAT‑restricted WAN links.
   • Baseline: Ray Distributed (v2.0) with default static routing and static tensor sharding.

Results & Findings

• Dynamic partitioning reduced memory fragmentation on RRAM arrays, allowing more of the on‑chip storage to be used for active tensors.
• Peer selection aware of NAT constraints cut unnecessary round‑trips, which directly contributed to the latency drop.
• Runtime re‑deployment kept hot‑spots balanced, preventing the “straggler” effect common in static distributed training jobs.

Practical Implications

• For AI Platform Engineers: SEDMA’s API can be layered on top of existing orchestration tools (Kubernetes, Docker Swarm) to add autonomous memory‑aware scaling without rewriting model code.
• Edge & IoT Deployments: The memory‑guided matrix processor is especially valuable for devices with emerging non‑volatile memories (e.g., RRAM, MRAM) where traditional static sharding would waste precious on‑chip space.
• Cost Savings: Higher memory utilization means fewer nodes are needed for a given model size, translating into lower cloud spend or reduced hardware footprint in data centers.
• Network‑Constrained Environments: Applications that must traverse NATs or operate over spotty WAN links (e.g., federated learning, remote inference) can benefit from the peer‑selection logic to keep traffic efficient.
• Continuous Optimization: Because the system learns from each run, organizations can expect performance to improve over time without manual tuning—a compelling proposition for MLOps pipelines that need to stay agile.

Limitations & Future Work

• Device‑Specific Calibration: The current implementation requires a profiling step to capture RRAM non‑idealities; automating this for arbitrary accelerators remains an open challenge.
• Overhead of Dual‑Memory Management: While the authors report net gains, the added monitoring and decision‑making logic introduces a modest CPU overhead that could be problematic on ultra‑low‑power edge nodes.
• Scalability Beyond 1 K Nodes: Experiments were limited to a few hundred heterogeneous nodes; the authors plan to evaluate the architecture on larger clusters and with more diverse network topologies.
• Security Considerations: Dynamic peer selection and container migration across NATs raise potential attack surfaces; future work will explore hardened communication channels and policy‑driven placement constraints.

Overall, the Self‑Evolving Distributed Memory Architecture offers a compelling blueprint for making large‑scale AI systems more memory‑efficient, latency‑aware, and self‑optimizing—qualities that are increasingly critical as models grow and deployment environments become more heterogeneous.

Authors

• Zixuan Li
• Chuanzhen Wang
• Haotian Sun

Paper Information

• arXiv ID: 2601.05569v1
• Categories: cs.DC
• Published: January 9, 2026
• PDF: Download PDF