Modular Memory Architectures

• Modular memory architectures are system-level designs that separate memory functions into distinct, specialized modules to enable scalable and efficient processing.
• They integrate short-term, long-term, and external memory components to support adaptive AI agents, high-throughput hardware, and resilient distributed systems.
• Their design employs explicit interfaces for writing, reading, and forgetting, striking a balance between rapid adaptation and robust, long-term retention.

Modular memory architectures are system-level designs in which memory is divided into interoperable, composable modules, each specialized for a distinct function, data type, or time-scale. This paradigm enables scalable, adaptive, and efficient memory behavior across hardware, software, and algorithmic domains. Modular memory architectures are central to high-performance computing, adaptive AI agents, quantum/classical hybrid systems, and resilient distributed platforms. By explicitly separating storage, management, retrieval, and update mechanisms, these architectures enhance scalability, mitigate catastrophic forgetting, allow plug-and-play adaptation, and provide principled robustness to hardware and task heterogeneity.

1. Formal Abstractions and Core Components

At the foundational level, modular memory architectures decompose memory into specialized modules, typically instantiated as:

• Working (Short-Term) Memory $W$: A bounded buffer for recent or transient state (e.g., immediate dialogue context, current batch in an accelerator).
• Long-Term/Episodic/Semantic Memory $L$: A larger, persistently managed repository for accumulated or abstracted knowledge (e.g., user profiles, model experience, external facts).
• External/Associative Memory: E.g., addressable storage in neural computers, non-volatile rack-scale memory, photonic cluster states as quantum memory slices.

Each module exposes three fundamental interfaces:

• Write $U(\cdot)$: Determines which information is stored, with policies to handle memory slot allocation and overwrite.
• Read $R(\cdot)$: Retrieves relevant items given a query, often via attention, content-based retrieval, or hierarchical search.
• Forgetting $F(\cdot)$: Implements pruning, eviction, or compression to control retention and ensure memory efficiency.

Operationally, system evolution at each time $t$ can be formalized as

$$\begin{align*} x_t \rightarrow w_t \in W_t \ r_t = R(L_t, q_t) \ \hat{y}_t = \text{Core}_t(x_t, w_t, r_t;\,\theta_t) \ L_{t+1} = F\left(L_t \cup U(w_t, \hat{y}_t, \text{feedback}_t)\right) \end{align*}$$

This structure underpins both adaptive AI systems (e.g., continual learning agents (Dorovatas et al., 2 Mar 2026), LLM-based memory systems (Wu et al., 2 Apr 2026)), and hardware platforms (memory slices (Asgari et al., 2018), modular FPGA controllers (Wijeratne et al., 2021), photonic/quantum modules (Shapourian et al., 2022, Chen et al., 18 Mar 2025)).

2. System Architectures: AI, Hardware, and Hybrid Domains

Continual Learning and LLM-based Agents

In adaptive AI, modular memory is the foundation for continual learning frameworks that leverage the synergy between In-Context Learning (ICL)—fast adaptation by attending to recent experience without parameter change—and In-Weight Learning (IWL)—slow, stable parameter update via replay and consolidation. The architecture operates in two regimes:

• External Interaction (ICL): At each input, $x_t$ is encoded into working memory $W_t$; long-term memory $L_t$ may be queried for relevant retrieval; the core model attends over $L$0; output is produced and feedback written back to $L$1.
• Internal Consolidation (IWL): Periodically, a controller samples episodes from $L$2 and updates model parameters $L$3 via gradient descent, optionally with regularization (e.g., EWC penalties).

This modular organization is mirrored in agent-centric LLM memory, which integrates working, episodic, and semantic modules (FIFO buffer, hierarchical tree, vector store) with explicit extraction, management, and retrieval interfaces (Wu et al., 2 Apr 2026).

Memory in Hardware Architectures

In hardware, modular memory appears as memory slices (Asgari et al., 2018)—self-contained DRAM plus compute blocks connected by programmable interfaces and wormhole interconnects. Slices are orchestrated such that data partitioning and computation scale linearly or super-linearly with the number of modules, offering fine-grained trade-offs in energy, throughput, and latency.

Similarly, modular SRAM designs (Ku et al., 2024) and configurable multi-port memory (Dhakad et al., 2024) demonstrate how modularity at the circuit/peripheral level enables increased parallelism (e.g., 4× bandwidth at 8% area overhead for quad-port wrappers) and area-efficient support for high-throughput ECC or DSP kernels.

Distributed, Quantum, and Disaggregated Systems

Quantum modularity is realized via partitioning large, circuit-depth-constrained LSTM gates into small variational circuit modules on separate QPUs. The outputs are recombined classically, supporting stable, scalable quantum sequence modeling on NISQ devices (Chen et al., 18 Mar 2025). Photonic quantum memory modules, using quantum dot emitters and passive delay/scattering circuits, enable deterministic graph state generation for modular, error-tolerant quantum storage (Shapourian et al., 2022).

Disaggregated modular memory at the rack scale, as in MODC (Keeton et al., 2021), uses fabric-attached NVM pools that persist across compute node failures. Programming and runtime support leverage lock-free, task-decomposed frameworks that maintain per-task state in NVM and enable fine-grained, resilient job rescheduling.

3. Core Methodologies and Mathematical Constructs

Key frameworks across domains are unified by several mathematical/algorithmic principles:

• Retrieval Functions: $L$4; implemented as nearest-neighbor search, vector similarity, hierarchical traversal, or LLM-aided selection.
• Write/Update Policies: $L$5; selection may be by recency, content novelty, or explicit controller logic.
• Eviction/Forgetting Policies: $L$6; FIFO, least-recently used, attention-based pruning, or learned controller.
• Stability-Plasticity Trade-off: In continual learning, loss

$L$7

balances rapid adaptation and retention.

Hardware and FPGA memory controllers partition address space and optimize scheduling (parallel batch sort, LRU caching, DMA for bulk), with explicit timing/resource formulas to manage modular scaling (Wijeratne et al., 2021). In memory slices, roofline models dictate performance as $L$8, exploiting local compute/memory bandwidth balance for scalable execution (Asgari et al., 2018).

4. Practical Applications and Empirical Insights

Applications span:

• Adaptive AI Agents: Modular memory agents demonstrate improved adaptation, reduced forgetting, and principled resource control. Position papers emphasize the necessity of concretely specifying retrieval, update, and forgetting controllers, and call for benchmarks that jointly evaluate rapid adaptation (ICL) and stable retention (IWL) under bounded memory (Dorovatas et al., 2 Mar 2026).
• High-Throughput Hardware: Modular hardware (slices, ported SRAM) achieve area and energy efficiency, with measured gains, e.g., ModSRAM delivers over 50% cycle reduction for large-bitwidth modular multiplications (Ku et al., 2024), while multi-port architectures outperform dedicated bitcell designs in area and flexibility (Dhakad et al., 2024).
• Quantum and Photonic Scenarios: Distributed quantum LSTMs scale circuit depth and qubit count by modularizing gates, yielding high accuracy and reduced noise. Modular photonic quantum memory reaches fault-tolerant error thresholds (0.53% for 3D RHG cluster states) and accommodates high photon losses when built directly (Shapourian et al., 2022, Chen et al., 18 Mar 2025).
• Resilient, Elastic Distributed Systems: MODC-style frameworks achieve near-zero overhead and rapid recovery (0.9% slowdown for a failure vs. up to 51% in MPI checkpointing), via decentralized, lock-free task/fault metadata in NVM (Keeton et al., 2021).

5. Advantages, Design Trade-offs, and Open Research Questions

Strengths

• Enables separation of adaptation timescales and memory function, reducing interference and catastrophic forgetting (Dorovatas et al., 2 Mar 2026).
• Scales independently in bandwidth, capacity, and energy efficiency via compositional hardware microarchitectures (Asgari et al., 2018, Dhakad et al., 2024).
• Enables specialized handling of multimodal, hierarchical, or task-specific memories in agent and quantum/classical hybrid settings (Wu et al., 2 Apr 2026, Chen et al., 18 Mar 2025).
• Facilitates rapid recovery and elasticity in distributed/disaggregated environments (Keeton et al., 2021).

Trade-offs and Limitations

• Controller logic for retrieval, update, and eviction in modular AI memory remains under-specified; meta-control and scheduling are unresolved (Dorovatas et al., 2 Mar 2026).
• In hardware, increased control-path complexity and limited simultaneous port scaling arise from peripheralized modular logic (Dhakad et al., 2024).
• Quantum systems lose global entanglement speedup as they modularize; increased classical synchronization/copying overhead is incurred (Chen et al., 18 Mar 2025).
• Benchmarks often fail to measure both rapid in-context learning and durable retention under memory constraints (Dorovatas et al., 2 Mar 2026).

Open Challenges

• Formal specification and data-driven discovery of retrieval/eviction/logical segmentation controllers in each domain.
• Fully integrating multimodal (text, vision, audio, tool) memory under unified modular APIs (Wu et al., 2 Apr 2026).
• Hardware/software co-optimization for runtime-parameterized modular architectures.
• Online continuous evaluation capturing human-like forgetting, memory consolidation, and adaptation.

6. Perspectives and Future Research Directions

The modular memory paradigm is converging towards unified frameworks where memory is not an undifferentiated pool but a collection of interoperating modules—each with explicit interfaces, capacity policies, and update schedules. Empirical studies show the superiority of hierarchical, multi-tier memory over flat buffers, the necessity of preserving raw interaction records alongside structured summaries, and that fine-grained or adaptive segmentation improves both retention and sample efficiency (Wu et al., 2 Apr 2026).

Future innovation is anticipated along several axes:

• Multimodal, Multitiered Memory: Integration of text, vision, audio, and tool traces in a modular, queryable memory with shared management primitives (Wu et al., 2 Apr 2026).
• Learned Memory Management: Neural and hybrid algorithms for semantic clustering, usage scoring, and non-destructive update.
• Bidirectional and Adaptive Retrieval: Dynamic routing over vector, tree, graph, or LLM-augmented search pathways, optimized per query.
• Efficient Compression and Expansion: Compression mechanisms for memory growth; reversible expansion of long-term memory for episodic/logical reconstruction.
• Benchmarking and Real-World Validation: Creation of datasets and metrics that stress both fast adaptation and robust knowledge accumulation at system scale.

In sum, modular memory architectures provide a principled basis for scalable, adaptive, and robust memory in both artificial intelligence and hardware systems, driving innovation across continual learning, LLM agents, quantum/classical hybrid platforms, programmable hardware, and resilient distributed computing (Dorovatas et al., 2 Mar 2026, Wu et al., 2 Apr 2026, Asgari et al., 2018, Wijeratne et al., 2021, Dhakad et al., 2024, Chen et al., 18 Mar 2025, Shapourian et al., 2022, Keeton et al., 2021).