EvolveLab Unified Codebase

• EvolveLab Unified Codebase is a modular, composable framework unifying memory and agent architectures across hardware and software stacks for systematic design-space exploration.
• It leverages well-parameterized building blocks to enable dynamic trade-offs in performance, energy, and functionality across diverse computational research domains.
• The platform supports automated, evolutionary search using analytic, simulation-based, and heuristic methodologies to optimize modular configurations efficiently.

The EvolveLab Unified Codebase represents an emergent organizing paradigm in computational research and systems engineering: memory and agent architectures built, navigated, and optimized as modular, composable design spaces. Drawing on recent hardware, accelerator, agentic, and simulation research, this unification leverages modularity—exposing discrete, well-parameterized building blocks at all stack levels (from memory bank circuits to memory-augmented agentic reasoning)—to accelerate exploration of performance, energy, and functionality trade-offs across disparate research domains. Architectures such as memory slice-based neural accelerators, multi-agent memory banks, hybrid DRAM–NVM systems, and configurable mixed-signal CIM pipelines describe formal abstractions, parameterizable modules, and systematic design-space navigation methodologies to systematically search and optimize these unified codebases.

1. Architectural Modularity Across Hardware and Agentic Stacks

The EvolveLab Unified Codebase synthesizes principles from several modular architectures:

• Bit-slice-inspired memory modules: Each “memory slice” combines a DRAM (HBM, HMC vault, or DDR DIMM), a programmable memory interface (PMI), a high-reuse systolic array, and a wormhole-switched network port (Asgari et al., 2018). Modular replication and interconnection of slices allow scalable and balanced capacity, bandwidth, and local compute, enforcing the analytic condition $$I_\text{slice} = P/B_\text{mem} \approx I_\text{app}$$ (arithmetic intensity match for Roofline scaling).
• Configurable banked memory for embedded systems: Reconfigurable many-core tiles instantiate 2 KB memory banks that can be grouped, overlapped, and re-purposed at runtime as direct-mapped or associative caches, scratchpads, or message-packet buffers. Each memory group is software-addressable, enabling dynamic data, instruction, and stack partitioning, with exposed primitives to manipulate routing, atomicity, and memory operations at the messaging level (Bates et al., 2016).
• Multi-layer, plug-and-play DRAM/NVM memory managers: Closed-form abstractions such as the HMMU expose four modules (DRAM interface, NVM interface, policy engine, and DMA migration), each with a compact set of parameters and clear internal state. Tuneable trade-off “knobs” (DRAM/NVM split, block size, cache fraction, adaptive migration threshold) allow movement throughout the performance vs. energy vs. endurance surface (Wen et al., 2020).
• Agentic modularity in memory-augmented LLM agents: Agent architectures such as LEGOMem and AgentSquare abstract procedural memory and reasoning support into instantiable modules. Procedural and runtime memory units (full-task, subtask, hierarchical, or retrieval-based) are indexed and retrieved by standardized embedding and query interfaces, enabling flexible memory allocation across orchestrators and agents, and evolutionary or recombinatorial search over module designs (Han et al., 6 Oct 2025, Shang et al., 2024).
• Plug-in computation- and device-level modules in CIM/AI accelerators: Simulation frameworks (e.g., MICSim, ZigZag) model devices as composable, Python-inheritable classes for each level—quantizer, digit-to-cell mapping, analog circuit, array hierarchy—permitting plug-and-play insertion of new quantizers, device models, crossbar circuits, and bank partitioning, then systematic design space exploration (Wang et al., 2024, Mei et al., 2020).

2. Formal Abstractions: Parameter Spaces, Interfaces, and Composition

Every EvolveLab modular codebase—whether targeting embedded DRAM/NVM, compute-in-memory, or multi-agent procedural memory—exposes a rich, well-defined parameter space. Critical abstractions include:

• Memory slice parameterization (example): $M$ (slice capacity), $B_\text{mem}$ (local bandwidth), $P$ (local compute), $R$/$C$ (PE array dimensions), $A$ (mm²), Pwr (W) (Asgari et al., 2018).
• Configurable banks: $N_\text{banks}$ (per-tile capacity), $N_\text{groups}$, split policies, group overlap, bank mode (scratchpad, cache), L0 FIFO/TLB sizes, replacement policy (Bates et al., 2016).
• HMMU knobs: DRAM:NVM ratio, block size ($B_s$), cache fraction ($f_{cache}$), migration threshold ($T$), DMA width, policy adaptation aggressiveness (Wen et al., 2020).
• CIM simulator layers: device-level (type, conductance, bit-depth), circuit-level (DAC/ADC, quantizer), array-level (bank sizes, mapping), topological (PE tile arrangement, sharing) (Wang et al., 2024).
• Agentic module interface: standardized write and retrieve calls (memory-state, current observation ↔ relevant memory items), applied to episodic, hierarchical, or event-driven memory stores (Shang et al., 2024, Han et al., 6 Oct 2025).

Formal interfaces (e.g., Python class inheritance, standardized input-output contracts, fixed-format memory units, or state–action API boundaries) ensure each module is exchangeable, extensible, and traversable through evolutionary or automated search.

3. Systematic Design-Space Exploration: Engines and Methodologies

A unified codebase is not simply a collection of static, swappable parts; it reproducibly enumerates, traverses, and evaluates the cartesian product of design choices. Techniques include:

• Analytic and simulation-based design-space enumeration: Pre-RTL or hardware-analytical frameworks (Aladdin for AMM (Sethi, 2020), MICSim for CIM/AI (Wang et al., 2024), DreamRAM for die-stacked DRAM (Cai et al., 13 Dec 2025)) sweep across parameters $(P_r, P_w, N, B, W)$, bank counts, ADC/DAC precisions, bank tilings, array topologies, and dataflow mappings, quantifying Pareto fronts in cycles, area, power, and energy-delay-product.
• Heuristic and constraint-based search: ZigZag's temporal and architecture generators assign loop blocks per operand at each memory level, systematically generating “even” and “uneven” mappings, then pruning by reuse, area, bandwidth, or application constraints (Mei et al., 2020).
• Evolutive and recombination search over agent modules: AgentSquare applies LLM-driven code generation and surrogate evaluation, evolving populations of agentic configurations by mutating and recombining module instances (planning, reasoning, tool-use, memory), using both true and performance-predicted eval (Shang et al., 2024).
• Empirical and composite metrics: Unified codebases report normalized performance (speedup, $S$), energy, cycle reduction, GFLOP/J, memory-on-miss rates, agentic task success, latency, VRAM, and retrieval/append/swap overheads, enabling cross-domain trade-off navigation (Bates et al., 2016, Braas et al., 13 Nov 2025, Wen et al., 2020).

4. Trade-offs, Performance Impacts, and Quantitative Empirics

Modular architecture enables continuous movement in a multi-dimensional trade-off surface. The empirical literature illustrates:

• Superlinear or near-linear scaling: Increasing slice count $N$ in a balanced NDP memory system realizes $S(N) \approx N^\alpha$ with $\alpha > 1$ at large $N$ due to reduced per-slice overhead, e.g., LSTM training throughput scales from 1.28 TFLOP/s/slice to 1.2 PFLOP/s at 256 slices, $88\%$ utilization (Asgari et al., 2018).
• Energy and area optimization: AMM approaches (e.g., LVT and HB-NTX) achieve $30$–$35$\% cycle savings over pure banking, with only $5$–$12$\% area overhead for low locality kernels; hybrid modular mappings allow within-domain adaptation ($P_r, P_w, B$) (Sethi, 2020).
• Latency/energy/endurance triage in mobile hybrid memory: Adaptive HMMU policies realize $0.88 \times$ runtime, $0.60 \times$ energy, and $0.80 \times$ NVM writes relative to all-DRAM, dictated by migration granularity and cache fraction (Wen et al., 2020).
• Agent/LLM-team performance: Joint memory placement in LEGOMem yields $+12$–$13$ pp absolute task success over baseline, with orchestrator memory critical for high-level planning and agent (subtask) memory boosting execution for weaker models (Han et al., 6 Oct 2025).
• Dialogue quality–latency scaling: Swappable memory modules in persona-driven SLMs enable sub-50 ms retrieval with $N \leq 1000$ entries, while model expressiveness scales with VRAM/$M$ (parameters) and retrieval/factuality metrics; full isolation vs. shared knowledge is realized via module allocation (Braas et al., 13 Nov 2025).

5. Guidelines for Modularity, Specialization, and Codebase Evolution

Several meta-lessons arise from the modular memory/agent unification literature:

• Expose, not obscure, parameter "knobs": Any fixed geometry (cache split, crossbar widths, memory slice array size) must become an externally set parameter, enabling automated or semi-automated sweeping.
• Partition for software/hardware synergy: Allow flexible assignment of modules (e.g., DRAM slices per compute island, or agentic memory per subtask processor) to best match workload arithmetic intensity and spatial/temporal locality (Asgari et al., 2018, Han et al., 6 Oct 2025).
• Preserve layering: interface/policy/metadata/migration: Decoupled layers (as in HMMU, DRAM-cache in gem5, or agentic reasoning/memory in LLM teams) facilitate clean, composable replacement and wider design-space coverage (Wen et al., 2020, Babaie et al., 2023).
• Automate exploration when possible: Evolutionary, heuristic, or sampling-based generators (as in AgentSquare, ZigZag, or MICSim average mode) outperform manual or static exploration, and permit generalization to new process technologies or agent team compositions (Shang et al., 2024, Mei et al., 2020, Wang et al., 2024).
• Cross-domain modularity: The same design principles transition from SoC analog/digital memory to AI accelerator mapping, from many-core routing to LLM reasoning agents, and from fine-grain ECC-friendly SRAMs to runtime-swappable agentic memory modules (Bates et al., 2016, Ku et al., 2024, Braas et al., 13 Nov 2025).

6. Unresolved Questions and Future Modular Design Research

Key open research directions within the EvolveLab Unified Codebase paradigm include:

• Memory condensation and lifelong learning: Dynamic agents and in-memory systems accumulate large non-parametric stores; strategies for condensation, pruning, or continual adaptation without catastrophic forgetting remain underexplored (Han et al., 6 Oct 2025).
• Hybrid parametric/external memory integration: Whether memories should be embedded within model weights (parametric) or kept externally addressable is unresolved; this is central in procedural memory agents and computational memory architectures (Han et al., 6 Oct 2025).
• Scalability and ecosystem adaptation: Modular designs must remain tractable as application and technology diversity expand—e.g., new tasks, tools, apps, or devices in both AI and transistor domains (Han et al., 6 Oct 2025, Cai et al., 13 Dec 2025).
• Cross-agent and cross-device sharing: Moving beyond per-module indices to shared representations, hybrid cache/scratchpad partitioning, or inter-agent knowledge routing could further expand Pareto-optimal regions (Han et al., 6 Oct 2025, Braas et al., 13 Nov 2025).
• Rigorous, unified benchmarks: The lack of cross-domain standard metrics slows rigorous comparison; coherent benchmarks for energy, latency, utilization, and task performance across agentic and hardware memory stacks remain needed.

EvolveLab's modular, design-space-centric approach is driving a convergence of hardware, system, and agentic research toward unified, navigable codebases, enabling reproducible, scalable exploration and evolution across the increasingly diverse landscape of memory- and agent-rich computational systems.