Adaptive Memory Control

• Adaptive Memory Control is a set of techniques that dynamically optimize memory usage, data placement, and access strategies by monitoring system state, workload, and historical data.
• It leverages decentralized self-organizing mechanisms, dynamic hardware-based controllers, and tiered memory systems to balance load, reduce latency, and enhance energy efficiency.
• The approach extends to memory-augmented neural network controllers and adaptive momentum optimizers, enabling robust real-time adaptation in deep learning and control systems.

Adaptive memory control refers to a class of techniques and architectures that dynamically optimize memory usage, data placement, and access strategies in computing or control systems by monitoring system state, workload, and historical data. It encompasses decentralized self-organizing mechanisms, memory-augmented control, dynamic migration and tiering strategies, and experience-based policies in both hardware and algorithmic domains. The following sections detail the core principles and leading research directions according to recent literature spanning manycore systems, memory controllers, adaptive control, and deep learning optimizers.

1. Decentralized and Autonomous Memory Management in Manycore Systems

Adaptive memory control is essential in highly parallel systems with dynamic workloads. The Self-aware Memory (SaM) architecture (Mattes et al., 2014) exemplifies decentralized, autonomous management with no central coordinator. Each memory module and core-associated management component monitors its state—such as allocation, load, and operational health—and exchanges periodic status updates with neighboring components. The system executes a multi-step optimization process, loosely structured as an extended MAPE (Monitor, Analyze, Plan, Execute) cycle:

• Decentralized monitoring and preprocessing: Gathering local operating statistics and communicating within a configurable neighborhood.
• Analysis using associative counter arrays: Aggregating access events or load until a threshold triggers an optimization proposition.
• Distribution of optimization algorithms: Algorithms are parameterized for specific goals (reducing latency, balancing load, or saving energy).
• Consensus-based validation: Local negotiation among involved nodes to select the best optimization without centralized arbitration.
• Execution and address migration: Agreed changes are effected, and state is updated (including memory migration and address remapping).

Mathematically, optimization triggering is defined by a counter condition such as $C_i \geq T$ (where $C_i$ is the counter for component $i$ and $T$ the operation threshold).

This approach enables scalability and flexibility by dividing management among modular, interacting nodes. Unlike central memory controllers, SaM avoids bottlenecks and single points of failure, providing continuous adaptation to dynamic and unpredictable workloads.

2. Dynamic Hardware-Based Adaptive Memory Control

Adaptive memory control in hardware targets latency reduction, data placement, and data migration for heterogeneous or hybrid memory systems.

• Adaptive-Latency DRAM (AL-DRAM) (Lee et al., 2016): Observes that DRAM timing is set for worst-case conditions (high temperature, slowest cell). By dynamically selecting timing parameters according to operational temperature and module characteristics, AL-DRAM shortens access times: at 55°C, tRCD, tRAS, tWR, and tRP can be reduced by 17.3–54.8% without errors. The memory controller adapts configuration without modifying DRAM hardware, yielding up to 14% performance gains for memory-bound multi-core workloads.
• Row Buffer Locality-Aware Controllers (Yoon et al., 2018): In DRAM–NVM hybrids, high penalty for row buffer misses in NVM is mitigated by continuously tracking row buffer miss counts per row in dedicated hardware (“stats store”). Hot data with frequent misses in NVM is adaptively migrated into DRAM, using a periodically tuned miss threshold to determine migration points. Migration cost-benefit is calculated as:

$$\text{Benefit} = N_{\text{reads}}^{\text{dram}} \cdot (\text{t}_{\text{read,pcm}} - \text{t}_{\text{read,dram}}) + N_{\text{writes}}^{\text{dram}} \cdot (\text{t}_{\text{write,pcm}} - \text{t}_{\text{write,dram}})$$

This policy improves system performance and energy efficiency, especially under mixed intensive workloads.

• Programmable Controllers (Wijeratne et al., 2021): Modular FPGA-based controllers enable reconfiguration of cache, DMA, and scheduler parameters per workload. Techniques such as batch request scheduling (using bitonic sort), programmable cache structures, and direct memory access engines are orchestrated to balance spatial/temporal locality and bulk transfer. Such controllers achieve up to 58% reduction in memory access time in deep learning and analytics workloads by adapting their internal memory handling engines dynamically.

3. Adaptive Memory Control in Tiered and Multi-Tenant Systems

Memory tiering strategies place the most frequently accessed (“hot”) data in fast memory and less used (“cold”) data in slower, larger tiers.

• ARMS: Adaptive and Robust Memory Tiering System (Yadalam et al., 6 Aug 2025): ARMS eliminates fixed policy thresholds by using dual exponentially weighted moving averages (EWMAs) per page for short-term and long-term access frequency:

$$\text{Score} = w_s \cdot \text{EWMA}_s + w_l \cdot \text{EWMA}_l$$

Weights $w_s, w_l$ are dynamically adjusted in response to workload phase changes detected by analyzing bandwidth (using the Page-Hinkley test). Promotion from slow to fast tier is additionally filtered using a cost-benefit formula:

$$\text{Benefit} = (p_{\text{score}} - q_{\text{score}}) \cdot p_{\text{hotage}} \cdot \Delta L$$

$$\text{Cost} = \text{promotion latency} + \text{demotion latency}$$

Adaptive, bandwidth-aware batched migrations further prevent congestion; batch size is adjusted based on available bandwidth.

• Per-Process Migration Decisions (Cho et al., 14 May 2025): In multi-tenant environments, adaptive migration algorithms monitor per-page “ping-pong” behavior (rapid promote/demote cycles) quantified by $\delta(t)$ and its slope. When persistent oscillations or instability are detected above a dynamic threshold, page migration is halted for that process until meaningful access pattern shifts are observed. This prevents wasteful migrations and improves performance particularly for migration-unfriendly workloads; single-tenant and multi-tenant scenarios both benefit, with up to 72% improvement over global migration strategies.

4. Adaptive Memory in Neural Network and Control Systems

Memory augmentation plays a pivotal role in adaptive control and machine learning for fast adaptation, improved tracking, and stability.

• Memory-Augmented NN Adaptive Controllers (Muthirayan et al., 2019, Muthirayan et al., 2019, Inanc et al., 2023): Neural network controllers are extended with external working memory modules, allowing the hidden layer outputs to be stored (write operation) and retrieved/reused (read operation) during future control steps. Memory-augmented outputs are expressed as:

$$u_{ad} = -\hat{W}^\top [\sigma(\hat{V}^\top \tilde{x} + \hat{b}_v) + M_r] - \hat{b}_w$$

The memory module is updated using forgetting and error-correcting dynamics. These architectures, including those using recurrent neural networks (RNNs) or LSTM, are proven to reduce estimation error and settling time in model-reference adaptive controllers, and provide bounded closed-loop stability (via Lyapunov analysis).

• Attention and Reallocation Mechanisms (Muthirayan et al., 2019): Controllers augmented with hard attention and attention reallocation mechanisms can focus on the most relevant memory locations and reallocate attention when relevance diminishes, preserving useful stored information and speeding adaptation following abrupt dynamics changes.
• Iterative Learning and Muscle Memory (Cheng et al., 18 Jul 2025): In robotics, adaptive controllers use iterative learning to refine feedforward torques, storing converged commands in a torque library linked to task features. Feedforward updates:

$$\tau_k^f(s) = \tau_{k-1}^f(s) + K_P^f \, e_{k-1}(s+\delta s) + K_D^f \, \dot{e}_{k-1}(s+\delta s)$$

The library enables rapid retrieval and interpolation for new tasks, achieving substantial tracking error reductions and efficient adaptation across diverse gaits and environments.

5. Adaptive Memory in Optimization and Learning

Momentum-based optimizers in deep learning use “memory” of past gradients to determine update directions.

• Adaptive Memory Momentum (Topollai et al., 6 Oct 2025): Rather than a fixed momentum coefficient (β), the method introduces a dynamically tuned momentum via a model-based proximal framework. Two surrogate “planes”—one from the current gradient, the other from the previous update (memory)—determine the optimal β at each step. The adaptive rule is:

$$\beta_t^\star = \operatorname{Clip}_{0,1} \left\{ \frac{ (\hat{f}(x_t) - f(x_t))(\lambda+1)/\eta - \langle d_t - g_t, g_t + \lambda d_t \rangle }{ \| d_t - g_t \|^2 } \right\}$$

This adaptivity allows the optimizer to reset momentum when the loss surface changes, ensuring faster and more robust convergence. Adaptive-momentum variants of SGD and AdamW consistently outperform classic fixed-momentum baselines without any learning rate or warmup tuning.

6. Theoretical Properties and Practical Implications

• Non-convex and Adversarial Domains (Zheng et al., 24 Sep 2025, Gradu et al., 2020, Zhang et al., 2021): Memory-augmented control algorithms incorporate memory-based potential fields or regret-based meta-controllers to enable escape from local minima and track time-varying optima efficiently. For instance:

$$V(x, M) = \alpha(x, M) V_{base}(x) + (1 - \alpha(x, M)) V_{mem}(x, M)$$

where $V_{mem}$ encodes historical trapping regions, and $\alpha$ smoothly transitions influence between base and memory. This theoretical framework, validated in stochastic optimal control and reinforcement learning, enables efficient adaptation in the presence of non-stationary or adversarial dynamics, with performance guarantees (e.g., sublinear adaptive regret bounds on all intervals).

• Experience-Driven Memory Structures in Multi-Agent RL (Chodhary et al., 22 Jul 2024): Dual-memory replay architectures combine short-term (episodic) and long-term (semantic) memories, modulated by equivalence-class grouping of state–action pairs. This controls memory growth, enhances learning efficiency, and allows scalable application to multi-agent environments such as traffic congestion control without excessive resource overhead.

7. Challenges, Limitations, and Outlook

Key technical challenges include optimal parameter selection for thresholding or migration policies, balancing memory overhead versus adaptation latency, and designing mechanisms robust to diverse workload dynamics or rapidly shifting regimes. There is no universal set of parameters that works optimally for all configurations (Yadalam et al., 6 Aug 2025). Solutions increasingly rely on self-tuning, multi-scale, and history-aware decision rules to obviate manual tuning.

Adaptive memory control continues to expand its reach across hardware, algorithmic, and cognitive-inspired frameworks. Future research directions include tighter integration of memory and learning mechanisms, hardware-efficient realizations, adaptive scheduling in more heterogeneous systems, and extending these ideas to partially observed or nonstationary environments. The goal remains real-time, robust, and efficient resource management in the face of unpredictable and dynamic conditions.