Memory-Integrated Reconfigurable Adapters (MIRA)

• Memory-Integrated Reconfigurable Adapters (MIRA) are systems that tightly couple memory with adaptive computational modules to enable dynamic, context-driven configuration.
• MIRA architectures achieve rapid non-volatile reconfiguration and high energy efficiency through integrated hardware and neural adapter designs, reducing data movement and latency.
• They balance parameter efficiency and reconfiguration speed while addressing challenges such as dynamic memory optimization and seamless hardware-software integration.

A Memory-Integrated Reconfigurable Adapter (MIRA) is a device or architectural framework that tightly couples memory with reconfigurable computation—either in hardware, such as analog or digital circuits, or in neural or algorithmic substrates—such that both the memory content and the computational function can be dynamically programmed, contextually retrieved, and efficiently adapted on demand. MIRA is grounded in the principle of physically or logically embedding adaptable modules (adapters) in proximity to storage, enabling rapid, context-sensitive reconfiguration. This paradigm underpins a broad spectrum of technologies, including memcapacitive analog filters for adaptive circuits, memory-augmented adapters for AI models, monolithic 3D FPGAs with embedded configuration memories, and programmable network-attached memory (NetDAM) architectures. Key goals of MIRA systems include non-volatile reconfiguration, high efficiency in area, latency, and power, and robust support for multi-task or multi-domain workloads with minimal data movement or catastrophic forgetting.

1. Architectural Principles of MIRA

MIRA systems are characterized by the co-design of memory elements and reconfigurable computational adapters, often realized as either physical hardware integration or parameter-efficient software modules.

• Hardware Embedding: In analog and digital hardware, as exemplified by ferroelectric memcapacitors (Yadav et al., 13 Nov 2025) and monolithic 3D FPGA fabrics (Waqar et al., 12 Jan 2025), memory elements (e.g., multistate capacitors, SRAM cells) are physically integrated into or stacked above programmable logic or analog elements. This reduces configuration wirelength, parasitics, and enables fast, localized state updates.
• Algorithmic and Neural Adapters: In neural architecture settings (Agrawal et al., 30 Nov 2025, Xu et al., 2023, Bini et al., 4 Dec 2025), adapters are parameter-efficient modules (often rank-constrained or LoRA) inserted at key points in a backbone model, augmented by associative or user-instantiated memory banks that store task- or domain-specific information. Memory access and update are realized via explicit key-value retrieval and gating, sometimes inspired by biological neuromodulation.
• Programmable Data Movement: In memory-centric networking fabrics (e.g., NetDAM (Fang et al., 2021)), MIRA manifests as network adapters coupled with on-board memory and logic capable of programmable in-memory and in-network compute via a custom instruction set, supporting dynamic resource partitioning and in-place data transformation.

This coupling facilitates not just the storage, but also fine-grained and rapid context-driven reconfiguration of functional behavior—be it circuit transfer characteristics, network communication patterns, or neural inference pathways.

2. Representative Implementations

MIRA Type	Memory-Adapter Linkage	Core Advantage
HZO Memcapacitor HPF (Yadav et al., 13 Nov 2025)	Capacitance state stores filter	Non-volatile, analog tuning
ViT + Hopfield LoRA (DG/CL) (Agrawal et al., 30 Nov 2025)	Layerwise expert adapters	Adaptivity, no catastrophic forgetting
MemLoRA for On-device LMs (Bini et al., 4 Dec 2025)	Swappable task adapters	Local, privacy-preserving memory ops
3D FPGA w/ BEOL AOS SRAM (Waqar et al., 12 Jan 2025)	SRAM atop pass-gate muxes	Reduced area, latency, power
Memory-aug. NMT Adapter (Xu et al., 2023)	Multi-granular external bank	Expressive, domain/on-the-fly modif.
NetDAM ISA+DRAM (network) (Fang et al., 2021)	On-board DRAM + compute engine	In-memory/in-network PIM, virtualization

A critical unifying feature is the dynamic selection and/or synthesis of adapter states or weights from the memory system, guided by contextual queries or reprogramming instructions.

3. Functional Mechanisms

3.1. Memory Writing and Reconfiguration

In digital neural instantiations (Agrawal et al., 30 Nov 2025), adapters are learned for specific domains/tasks and stored as value slots (θ) in layer-wise associative memory tables, indexed by contextually learned keys. In analog hardware (Yadav et al., 13 Nov 2025), multi-level states of a ferroelectric capacitor are written by specific voltage pulses, mapping filter characteristics to memory polarization wells.

3.2. Memory Retrieval and Application

Hopfield-style soft attention reads over stored adapter keys yield affine mixtures of LoRA updates for neural models, allowing per-sample customization without updating the main backbone (Agrawal et al., 30 Nov 2025). In memory-augmented translators (Xu et al., 2023), network states query user-specific example phrase banks for style/domain-conditioned retrieval. For hardware, programmed memory states are directly sensed as analog (e.g., capacitance) or digital (e.g., configuration bit) values, instantly modifying operational characteristics.

3.3. Bidirectional and Task-Specific Control

Several MIRA designs enable bidirectional reconfiguration: in hardware, voltage polarity can shift analog device states (supporting potentiation/depression analogues (Yadav et al., 13 Nov 2025)). In multi-stage memory AI pipelines, task-specific adapters are dynamically loaded/unloaded at runtime with negligible overhead (Bini et al., 4 Dec 2025), supporting stage-local specialization and rapid role switching.

4. Empirical Evaluation Across Domains

• Analog/RF/Neuromorphic Circuits: Ferroelectric memcapacitors achieve >8 nonvolatile capacitance states within a 24~pF window, tuned by ±3 V pulses, with >$10^5$ s retention and $10^6$ cycles endurance. High-pass filter cutoff frequency is tuned over a 4.4 kHz range (∼9.7 %) (Yadav et al., 13 Nov 2025).
• Monolithic 3D FPGA Fabrics: BEOL-stacked AOS SRAM and pass-gate placement enables 3.4× area-time$^2$ reduction, 27% lower latency, and 26% lower power versus 7 nm CMOS FPGAs (Waqar et al., 12 Jan 2025).
• Memory-Augmented Language/Multimodal Models: MemLoRA adapters for SLMs/SVLMs on LoCoMo benchmarks achieve text QA judge scores up to +90% over unmodified SLMs and match or exceed much larger LLM baselines, with only 0.5–2 GB additional storage and subsecond per-operation latency (Bini et al., 4 Dec 2025).
• Cross-domain/continual learning: MIRA with associative adapters (on ViT-B/16 CLIP backbones) yields state-of-the-art accuracy in domain generalization (e.g., 97.01% on PACS) and continual learning (e.g., 83.39%/93.89% on CORe50 CIL/DIL) with minimal forgetting, outperforming strong baselines such as ICON, CODA-P, and L2P (Agrawal et al., 30 Nov 2025).
• Networked Computing: NetDAM achieves sub-microsecond wire-to-wire latency (< 1 µs), sustaining up to 86 Gb/s per link and a 7× speedup on Allreduce versus standard RoCEv2 RDMA using in-memory compute and virtualized pooled DRAM (Fang et al., 2021).

5. Biological and Theoretical Foundations

Biological inspiration for MIRA architectures is direct: neuromodulatory overlays in cortical microcircuits dynamically reweight shared neural substrates via associative, context-dependent signaling (dopaminergic/cholinergic modulation) (Agrawal et al., 30 Nov 2025). The analogy is formalized as task-specific low-rank modulations (adapters) contextually gated via attractor memory reads, supporting cortical flexibility (rapid switching), memory retention (no catastrophic forgetting), and robust adaptation to domain/task shifts.

In theoretical computing, these architectures instantiate a fusion of von Neumann and in-memory compute paradigms. The inclusion of associative or multi-granular memories breaks parameterization bottlenecks while retaining modularity and safety (e.g., by freezing backbones), mirroring non-parametric overlays in biological systems and enabling rapid domain or context adaptation.

6. Design Trade-offs and Performance Considerations

• Parameter/Storage Efficiency: MIRA-style adapters add only a small fraction of parameters per task/domain (e.g., $2 rd$ per LoRA adapter vs. full $d × k$ updates), leveraging non-parametric memory for flexibility. Physical co-location of memory and logic similarly collapses device footprint.
• Reconfiguration Latency: Adapter swaps in neural MIRA take <1 ms; analog hardware reconfiguration is set by polarization switching, typically μs-scale or faster (Yadav et al., 13 Nov 2025, Bini et al., 4 Dec 2025). FPGA configuration via BEOL-stacked memory reduces control path latency, scaling with vertical integration (Waqar et al., 12 Jan 2025).
• Scalability: Neural and algorithmic MIRA systems saturate at 5–10 adapters per domain/task, with a “memory mosaic” sufficient for most observed context separations (Agrawal et al., 30 Nov 2025). Hardware memory size grows linearly with configuration granularity but may be mitigated by pruning or quantization.
• Application Range: MIRA is effective in ultrafast, low-power embedded signal processing (e.g., RF/neuromorphic front-ends), large-scale AI (continual/multimodal learning, memory-augmented LMs), and high-performance networking (memory-attached compute), with robust empirical gains across all domains cited.

7. Limitations and Future Directions

Open research questions include dynamic optimization of memory content (learned memory selection/pruning), deeper multimodal integration (image, text, and analog signals), hardware support for transactional/consistent memory-logic interplay, and in-network/adapter security with fine-grained access control. Further, system-level integration with domain-specific fabrics (CXL, P4/SR switches) and dynamic partial reconfiguration (e.g., hot-swappable DSA kernels) is nascent.

A plausible implication is that as AI, reconfigurable hardware, and distributed computing converge, MIRA will form a foundational architecture for unified, adaptive, memory-rich systems that support lifelong learning, heterogeneous domain operation, and energy-scaled performance across the computing stack (Agrawal et al., 30 Nov 2025, Bini et al., 4 Dec 2025, Fang et al., 2021, Waqar et al., 12 Jan 2025).