Dynamic Memory Systems

• Dynamic Memory Systems are adaptive computational frameworks that modify memory allocation and data representation based on workload and environmental changes.
• They incorporate principled models, feedback controls, and hardware innovations like augmented SRAM and memristive devices to optimize bandwidth and performance.
• These systems significantly enhance resource utilization in high-performance, deep learning, and distributed applications by dynamically tuning memory policies.

Dynamic memory systems are computational architectures, hardware designs, and software frameworks that adapt memory allocation, management, or data representation in response to workload, system, or environmental variations. Such systems are essential in high-performance computing, real-time embedded platforms, distributed AI infrastructures, advanced dataflow applications, and cognitive agent models. Their core function is to optimize memory utilization, bandwidth, and accessibility under time-varying, structurally diverse, or contention-prone scenarios.

1. Foundational Models and Theoretical Frameworks

Dynamic memory systems require principled models to guarantee performance, correctness, or schedulability under changing memory access patterns and allocation regimes.

Real-Time Multicore Memory Bandwidth Scheduling:

In multicore real-time platforms, contention over shared memory is a key bottleneck. The system model typically consists of $m$ homogeneous cores, each pinned to a statically scheduled real-time task, and a hardware memory budget regulator. Each core $i$ is assigned a fixed or dynamically changing budget $q_i$ of main-memory transaction slots per regulation period $P$. Dynamic bandwidth assignment is modeled as a sequence $\mathcal{S} = \{B^1,...,B^N\}$ of intervals, with per-core budgets and durations. Determining the worst-case response time for a task becomes a constrained maximization over distributed memory requests: maximize cumulative stall under scheduler-imposed bandwidths, leading to a fixed-point iteration involving concave per-period stall curves. Efficient greedy algorithms using the piecewise-linear, concave structure of the stall functions provide low-polynomial time complexity for the analysis. Main results formalized via convergence theorems show that schedulability under memory contention can be dramatically improved by dynamic (rather than static) bandwidth regulation (Agrawal et al., 2018).

Principled Dynamic Memory Extensions:

Dynamic memory extension systems in virtualization or containerized servers (e.g., DMX in Memory1) often model per-page access "hotness" using exponential moving averages, and enable batched migration between fast memory (DRAM) and slower, high-capacity media (e.g., NAND). Control policies are designed to maintain operation below memory-pressure thresholds, using page eviction/promotion decisions to optimize overall performance and maintain performance-critical quality-of-service (Rellermeyer et al., 2019).

Design for Hardware Heterogeneity:

Hybrid persistent memory architectures (e.g., DRAM + Optane DCPMM) must dynamically place or migrate pages to maximize the performance–cost ratio given the striking read/write asymmetry and constrained bandwidth of NVM. Here, two-threshold control (DRAM occupancy $\alpha$, DCPMM write bandwidth $\beta$) triggers migrations, with page-type classification (read-intensive, write-intensive, cold) derived from PTE access bits. Implementation in HyPlacer yields up to $11\times$ performance increase over Linux-default policies (Marques et al., 2021).

2. Dynamic Memory Architectures and Microarchitectural Innovations

Memory–Compute Fusion:

Dynamic Computing Random Access Memory (DCRAM) demonstrates microarchitectures where memcapacitive cells seamlessly integrate storage with digital logic. These cells enable both volatile and non-volatile state retention (via tunable analog charge), sub-nanosecond access, and in-memory polymorphic logic by programming only the pulse amplitudes and connectivity. Massively parallel page-wide logic and dynamic reconfiguration via voltage patterns eliminate the von Neumann bottleneck and scale with DRAM manufacturing processes (Traversa et al., 2013).

Augmented SRAM:

The Augmented Memory Computing (AMC) scheme introduces SRAM cells (8T/7T topologies) capable of toggling between static (SRAM) and dynamic (DRAM-like, ternary) modes. An 8T cell can act as standard SRAM (static bit) or, in augmented mode, store an additional dynamic bit, achieving $2\times$ capacity with moderate area/energy penalty ($\sim$25–250$\mu$s retention, temperature dependent). AMC is compatible with in-memory computing, allowing joint storage and computation with dynamic capacity scaling in real time (Sheshadri et al., 2021).

Memristive Devices and Dynamic Models:

RRAM/ReRAM and filamentary mem-resistor arrays are analyzed by modeling the system state as a coupled pair of dynamic spatial variables: conductive-bridge gap and Schottky barrier width. The result is a nonlinear dynamic system with frequency-dependent hysteresis and dispersive impedance, suitable as a physical substrate for reconfigurable and memory-intensive computing (Mouttet, 2011).

3. Workload-Adaptive and Feedback-Controlled Allocation

Online Feedback Controllers in HPC:

Dynamic in-memory storage controllers (e.g., DynIMS) dynamically resize the execution/storage split in main memory for HPC clusters. A discrete-time feedback law manages the in-memory cache to maximize hit ratio (up to $75\%$), minimize swap, and maintain low latency for both compute and data-intensive tasks. The control loop adapts at $\leq100$ms granularity, enforcing system-wide utilization bounds and reallocating capacity on sub-second timescales, yielding up to $5\times$ performance improvement over static allocation (Xuan et al., 2016).

Dynamic GPU Memory Management:

In deep-learning multi-job GPU environments, dynamic scheduling at tensor granularity (TENSILE) addresses cold-start, cross-iteration peaks, and real-time latency variance by actively profiling, predicting, and recomputing tensor-access timelines. Cooperative scheduling orchestrates swap-in/out and recomputation events, resulting in up to $27–52\%$ memory savings and minimal runtime overhead over prior art (Zhang et al., 2021). Server-class LLM serving frameworks further leverage CUDA virtual memory APIs (e.g., vAttention) to dissociate virtual addressability from physical memory commitment, minimizing KV cache fragmentation while retaining kernel simplicity and enabling negligible overhead ($1\%–5\%$ faster than paged variants) (Prabhu et al., 2024).

4. Evolutionary and Automated Synthesis of Dynamic Memory Managers

Emerging workloads, especially in embedded systems, require DMMs tuned for specific latency, memory, and energy cost profiles. Recent work uses grammatical evolution (GE) to construct and optimize DMMs from a search space defined over block-splitting/coalescing policies, allocation mechanisms (first-fit, best-fit, buddy), and data structures (DLL, SLL, BTREE) (Risco-Martín et al., 2024).

Parallel Evolutionary Algorithms:

To accelerate and improve DMM synthesis, parallel frameworks (using DEVS/SOA) distribute the evaluation of candidate DMMs via simulation, achieving nearly linear speed-up (up to $86\times$) and $36\%$ overall quality improvement relative to universal allocators and sequential GE (Risco-Martín et al., 2024).

5. Distributed, Dual-Memory, and Layered Dynamic Memory Systems

Self-Evolving Distributed Architectures:

The Self-Evolving Distributed Memory Architecture (SEDMA) coordinates dynamic memory management across computation (RRAM-based matrix partitioning), communication (memory-aware peer selection and routing), and deployment (job scheduling/Kubernetes reconfiguration). A dual-memory scheme tracks long-term performance (strategic partitioning, peer weights) and short-term statistics (current utilization), enabling adaptation both on fast and slow timescales. Empirical results show $87.3\%$ memory utilization and $30.2\%$ lower communication latency than prior distributed frameworks (Li et al., 9 Jan 2026).

Dual Memory for Spatio-Temporal Decoupling:

Mem4D demonstrates dual-memory systems in dynamic scene reconstruction by separating Transient Dynamics Memory (TDM: high-frequency, short-term) and Persistent Structure Memory (PSM: low-frequency, long-term), allowing accurate modeling of both rapidly moving objects and static backgrounds. Specialized readout blocks interleave attention to TDM and PSM, maintaining sharp dynamic details without geometric drift, and achieving $21.6\%$ improvement over baselines in scale-metric depth reconstruction (Cai et al., 11 Aug 2025).

Dynamic Spatio-Semantic Mapping:

Robot and embodied agents leverage dynamic 3D voxel memories with per-voxel semantic features (e.g., CLIP), continuously updating with add/remove operations informed by streaming RGB-D input. DynaMem achieves $70–74.5\%$ success rates in open-vocabulary object localization and manipulation in dynamic real-world scenes, significantly outperforming static memory baselines (Liu et al., 2024).

6. Dynamic Memory for Cognitive, Intelligent, and Agentic Systems

Learnable Dynamic Memory for Agents:

AtomMem reframes memory management as a sequential decision process over atomic CRUD (Create, Read, Update, Delete) operations, mapping to explicit external memory state $\mathcal{M}_t$. Policies are learned via supervised fine-tuning and reinforcement learning (GRPO), allowing the agent to autonomously optimize memory behaviors for task requirements, outperforming static workflows by $2$–$5$ points in long-context QA and demonstrating the criticality of Update, Delete, and integrative scratchpad operations. This establishes CRUD decision processes as a blueprint for future dynamic agentic memory models (Huo et al., 13 Jan 2026).

Memory-Driven Continual Learning:

Feedback-driven dynamic memory stores user-supplied corrections or facts in a continually growing corpus, indexed for retrieval at prediction time. Systems such as TeachMe show continual performance improvement (up to $15\%$ gain) on QA tasks without retraining, even as the core LLM remains frozen, via explicit memory augmentation and forced context integration during inference (Mishra et al., 2022).

Dynamic Energy-Based Sequential Memory:

EDEN formalizes a dynamic memory capable of high-capacity sequence storage by coupling a fast "feature" population with a slow "modulatory" population in an evolving energy landscape. The model achieves exponential sequence capacity $O(\gamma^N)$, a dynamic phase transition from static to sequential recall, and temporal cell activity patterns reminiscent of neural time/ramp cells, unifying static associative and dynamic chain memory in a single analytic framework (Karuvally et al., 28 Oct 2025).

7. Limitations, Trade-offs, and Open Challenges

Dynamic memory systems face characteristic trade-offs:

• Schedulability versus Complexity: Worst-case analysis is tractable under homogeneity and static release assumptions, but the models do not encompass arbitrary request reordering or full CPU-memory co-scheduling (Agrawal et al., 2018).
• Hardware Constraints: Device non-idealities (e.g., retention, bandwidth, access latency in augmented SRAM or Optane PM) limit dynamic operation windows and update rates (Sheshadri et al., 2021, Marques et al., 2021).
• Policy Tuning: Multi-objective optimization often requires empirical or evolutionary search, as no single policy best serves all applications or hardware scenarios (Risco-Martín et al., 2024, Risco-Martín et al., 2024, Xuan et al., 2016).
• Scalability: Indexing, migration, and decision overheads become pronounced at scale, especially in distributed or web-scale deployments (Li et al., 9 Jan 2026, Mishra et al., 2022).
• Conflict Resolution: In user-feedback-driven memory, persistent contradictions must be managed to avoid degraded reasoning or inconsistency (Mishra et al., 2022).
• Forecasting versus Reactivity: Most scheduling frameworks are reactive; pre-emptive or workload-forecasting strategies remain underexplored (Xuan et al., 2016).

Continued progress depends on deeper integration across system stack layers, further analytics for response-time/Pareto trade-offs, and more biologically inspired and reinforcement-learned adaptive memory mechanisms.