---
title: Module-aware Architecture Refinement (MAR)
url: https://www.emergentmind.com/topics/module-aware-architecture-refinement-mar
type: topic
---

# Module-aware Architecture Refinement (MAR)

Module-aware Architecture Refinement (MAR) is a unified framework for constructing energy-efficient large language models (LLMs) that combine linear-time sequence modeling via state space models (SSMs), activation sparsification through adaptive spiking neurons, and spike-aware bidirectional distillation. MAR targets the dominant sources of compute and energy consumption in contemporary neural architectures—quadratic-complexity self-attention and dense feed-forward networks (FFNs)—by refining their implementation at the module level without sacrificing model expressivity or performance. Extensive evaluations demonstrate that MAR restores a large fraction of dense model accuracy while significantly lowering inference energy requirements, outperforming other efficient models both in scale-matched and parameter-larger regimes [2601.21503].

## 1. Motivation and Architectural Targets

The principal inefficiencies in standard Transformer-derived LLMs arise from (i) the $O(N^2)$ complexity of self-attention, which impedes scalability to longer sequences, and (ii) the dense FFN sublayer, which, in practice, often dominates energy expenditures for non-extreme sequence lengths (e.g., ≤5,000 tokens). Direct substitutions such as low-rank or quantized FFNs frequently induce excessive accuracy loss. The MAR framework was developed to eliminate the quadratic bottleneck of attention entirely via linear-time models and to drastically reduce FFN costs by sparsifying activations with negligible performance degradation [2601.21503].

## 2. Two-stage Refinement Pipeline

MAR applies distinct module-level interventions in a staged architecture:

**Stage 1: Linear-Time Sequence Modeling with SSMs**

Self-attention layers are replaced by the discrete Mamba-2 SSM modules introduced in the Llamba architecture. These modules model an input sequence $\{u_t\}_{t=0}^{T-1}$ using the recurrence: 
$$
x_t = Ax_{t-1} + Bu_t; \quad y_t = Cx_t + Du_t,
$$
with learned matrices $A,B,C,D$, and hidden state $x_t \in \mathbb{R}^d$. This approach yields $O(Nd^2)$ computational and memory cost for sequence length $N$ and hidden size $d$, avoiding quadratic scaling in $N$ [2601.21503].

**Stage 2: Activation Sparsification via Spiking Neurons**

Sparse computation is achieved by interposing spiking neurons prior to each linear projection—four such placements per decoder layer: inputs/outputs for both SSM and FFN modules. These neurons convert pre-activations to spike trains, so subsequent matrix operations require only accumulate (AC) steps rather than dense multiply-accumulate (MAC), reducing per-operation energy from 4.6 pJ (MAC) to 0.9 pJ (AC), as detailed in the adopted energy model [2601.21503].

## 3. Spiking Neuron Design and Information Preservation

Naively introducing binary (0/1) spiking neurons poses two challenges: (a) reduced information density due to infrequent spike emissions, and (b) temporal misalignment between SSMs’ dynamics and discrete spikes. MAR addresses these with the Adaptive Ternary Multi-step Neuron (ATMN):

**Adaptive Ternary Multi-step Neuron (ATMN):**
- Extends spiking neuron outputs to the ternary domain $s_t \in \{-1,0,1\}$.
- Utilizes an adaptive, neuron-wise learnable threshold $V_{\mathrm{adaptive}} = e^a \ge 0$ (with $a$ learned).
- Stores post-spike residual membrane potential $u_t$, sustaining temporal continuity across activations.

The ATMN’s enhanced representation and learnable thresholds significantly increase per-spike information throughput and firing adaptability, facilitating seamless integration with the continuous-time dynamics of SSMs [2601.21503].

## 4. Spike-aware Bidirectional Distillation

Transferring task performance from a dense teacher (Llamba) to a sparsified, spiking MAR student is complex due to spike train burstiness and altered representational geometry. MAR introduces the Spike-aware Bidirectional Distillation Strategy (SBDS):

- **Logit-level bidirectional loss:** Blends the standard $\mathrm{KL}$ divergence with a reversed term balancing the teacher’s and student’s predictive distributions, parametrized by $(\alpha, \beta) = (0.2, 0.7)$ for best empirical performance:
$$
\mathcal{L}_1(p\Vert q) = \sum_{k=0}^{D-1} [\alpha p(k) - \beta q(k)] [\log p(k) - \log q(k)]
$$
- **Feature-level pre-normalization alignment:** Matches hidden activations immediately after RMSNorm, minimizing $L_2$ discrepancy between teacher and student representations:
$$
\mathcal{L}_2(h^j_{t,l}, h^k_{t,l}) = \|\mathrm{PreNorm}(h^j_{t,l}) - \mathrm{PreNorm}(h^k_{t,l})\|_2
$$
- **Overall distillation objective:** Averages logit and feature losses across time, token, and layer dimensions.

Ablation results indicate each distillation augmentation—ternary neuron (ATMN), bidirectional logit loss, and pre-norm feature alignment—substantially contributes to performance restoration [2601.21503].

## 5. Experimental Assessment and Benchmarking

The Llamba-1B (Mamba-2, 1.4 B parameters) serves as the foundation for MAR model construction, trained on 7 B tokens (GenQA, OpenHermes 2.5, InfinityInstruct) for one epoch. Performance is evaluated via zero-shot accuracy on PIQA, BoolQ, Winogrande, HellaSwag, ARC-Easy, and ARC-Challenge. Energy costs adopt estimates from Horowitz (2014).

| Model             | Size  | Spiking? | Avg. Zero-Shot Acc. |
|-------------------|-------|----------|---------------------|
| LLaMA             | 1.3B  | No       | 61.80%              |
| Llamba (teacher)  | 1.4B  | No       | 61.88%              |
| Bi-Mamba          | 1.3B  | No       | 49.38%              |
| SmoothQuant       | 1.3B  | No       | 54.93%              |
| TinyLLaMA         | 1.3B  | No       | 55.91%              |
| SpikeLLM          | 7.0B  | Yes      | 52.48%              |
| MAR (Ours)        | 1.4B  | Yes      | 57.20%              |

MAR exhibits substantial parameter-efficiency, matching or outperforming non-spiking models of similar scale and exceeding a 7 B parameter spiking LLM baseline (SpikeLLM). Ablations demonstrate the necessity of each architectural and training refinement: incorporating ATMN (from 46.28% to 55.20%), adding reverse KL (55.46%), and pre-norm alignment (57.20%). Feature alignment post-normalization was found suboptimal [2601.21503].

Energy consumption measurements confirm that MAR’s total energy scales much more favorably with sequence length than LLaMA and Llamba, achieving up to 30–40% lower energy use at longer contexts (≤4,000 tokens).

## 6. Design Insights and Practical Implications

The empirical results signify that module-level interventions—specifically, integrating linear-time SSMs in lieu of self-attention, augmenting FFNs with adaptive spiking neurons, and applying targeted bidirectional spike-aware distillation—enable energy reductions without incurring major accuracy penalties. Key mechanisms include: (1) $O(N)$ sequence modeling removing attention bottlenecks; (2) ternary, adaptively-thresholded spikes preserving information density; (3) pre-norm, reverse KL-based distillation addressing temporal and representational mismatches. This composition supports the creation of deployable, low-power LLMs suitable for on-device or resource-constrained applications, achieving full or near-full performance under strict energy and latency constraints [2601.21503].

## 7. Comparative Context and Significance

Module-aware Architecture Refinement advances the field of efficient language modeling by demonstrating that principled, module-targeted interventions can closely approach dense model accuracy in much more energy- and resource-impoverished environments. Benchmarks confirm that MAR’s performance is robust to sequence length scaling—reflecting the practical gains from linearized sequence processing and sparse activation protocols—and that it outperforms both non-spiking and larger spiking competitors when parameter count and data regime are held constant. These findings indicate that MAR constitutes a substantive framework for constructing the next generation of practical, high-performance, resource-efficient LLMs [2601.21503].

Source: https://www.emergentmind.com/topics/module-aware-architecture-refinement-mar