Arch-Router Framework Overview

Updated 23 October 2025

Arch-Router Framework is a modular, integrated approach that defines efficient routing methods for FPGA NoCs, multi-LLM systems, and Mixture-of-Experts architectures.
It employs hybrid designs and explicit parameterization to optimize throughput, reduce latency, and improve expert specialization in various applications.
The framework supports rapid prototyping and scalable deployment in SoCs, conversational AI, and advanced neural models using dynamic routing policies.

The Arch-Router Framework is a term that encompasses a range of architectures and methodologies for routing in computational networks, broadly spanning high-performance on-chip communication systems, preference-aligned LLM selection, and advanced Mixture-of-Experts (MoE) router design in large neural models. This entry surveys the principal concepts and instantiations of the Arch-Router Framework, tracing its evolution and technical contributions across these domains.

1. Hybrid On-Chip Router Architectures for FPGAs

The original usage of “Arch-Router Framework” is rooted in high-performance, hybrid two-layer routers for FPGA-based Networks-on-Chip (NoCs) (Ezhumalai et al., 2010). The core microarchitecture integrates two communication paradigms within a single router:

Packet-switched (P-layer): Provides traditional, flow-controlled, packet-based routing between routers and IP cores using request/grant arbitration and virtual cut-through.
Circuit-switched (C-layer): Supports time-multiplexed, scheduled, point-to-point data transfers directly between locally attached IP cores, bypassing the computational and arbitration overhead of packet switching for local communications.

Data directed between routers traverses the P-layer, incurring serialization latency characterized by $F/b$ (where $F$ is the number of flits per packet and $b$ the channel width). For intra-router communication among local IP cores, the C-layer eliminates packetization, instead leveraging a centralized arbiter to configure a multiplexer-based cross-point matrix for predictable, low-latency transfers.

This design achieves an average 20.4% NoC bandwidth improvement (peak 24%) relative to traditional NoCs, with area-to-bandwidth scaling favorably enabled by the hybrid router’s parameterization over number of ports, channel width, and bRAM depth. The MoClib Library provides parameterized component instances supporting rapid design iteration and topological scaling. The framework’s modularity and parameterization undergird its suitability for modern, performance-centric, FPGA-based SoC deployments.

2. Preference-Aligned Routing in Multi-LLM Systems

With the proliferation of LLMs optimized for distinct domains and capabilities, preference-aligned routing has become critical for model selection systems (Tran et al., 19 Jun 2025). The Arch-Router framework in this context is instantiated as a compact 1.5B parameter generative model that maps user queries to routing policies along a Domain–Action taxonomy (e.g., {finance, summarization}).

The processing pipeline:

Router module $\mathcal{F}$ : Given a user query $q$ and the set of available route policies $\mathcal{C}$ (expressed in natural language), $\mathcal{F}$ predicts the optimal route identifier $c$ by minimizing cross-entropy loss over $(x, c_\text{true})$ pairs, where $x$ is a prompt embedding both query and route descriptions.
Mapping function $\mathcal{T}$ : Translates the selected route policy $c$ to a backend LLM $M = \mathcal{T}(c)$ . This decoupling allows seamless addition of new models or policies by updating $\mathcal{T}$ , without retraining $\mathcal{F}$ .

Using supervised fine-tuning over 43,000 samples, the model captures domain/action specificity and can dynamically integrate policy updates via in-prompt route descriptions. On multiple conversational benchmarks, Arch-Router achieves 93.17% overall routing accuracy, outperforming proprietary systems (e.g., GPT-4o, Claude-sonnet-3.7) by an average margin of 7.71%. Latency benchmarks indicate a mean of 51±12 ms—comparable to or surpassing existing competitor frameworks. The framework thus enables high-accuracy, low-latency, user-preference-aligned model selection in multi-LLM environments.

3. MoE Router Architectures: Comparative Analysis and Design Principles

MoE architectures scale large neural networks by conditionally routing tokens to specialized “expert” subnetworks via a router module (Harvey et al., 19 Jun 2025). The Arch-Router Framework, in this setting, serves as a comparative and experimental platform for evaluating fundamental router architectures and their trade-offs:

Router Type	Expressiveness	Parameter Overhead	Latency (ms/token)	Routing Entropy
Linear	Low (inner prod.)	Minimal (∼6K)	0.07	Mid/high (distributed)
Attention	High	Moderate	Moderate	High (distributed)
MLP	Medium/High	High (∼101K)	Higher	Mid/variable
Hybrid	Med-High	Depends	Higher	See paper
Hash	None (determin.)	0	~85	Low (deterministic)
MLP-Hadamard	High, structured	High (∼101K)	Highest	Lowest (concentrated)

Linear/Hash Routers: Offer low computational overhead and fast inference but are limited in expressiveness. Hash-based deterministic assignment can cause severe load-imbalance.
MLP, Attention, MLP-Hadamard Routers: Allow more complex, context-dependent assignment of tokens to experts. The novel MLP-Hadamard router gates MLP-derived activations with the raw token input via element-wise product, leading to highly concentrated, sparse routing (entropy ≈ 1.10) and robust expert specialization.
Auxiliary Losses: Encourage balanced expert utilization, with load balancing and mean top-k probability as key metrics.

The framework demonstrates that trade-offs between latency, parameter efficiency, and routing precision are inevitable, with specific choices dependent on scaling targets and application requirements. Replacement and fine-tuning of routers in large, quantized MoE models (e.g., Qwen1.5-MoE, with 60 experts) are supported, leveraging helper functions and parameter-efficient fine-tuning (PEFT/LoRA) under strict memory constraints.

4. Router Upcycling and Attention-based Collaborative Routing

Router Upcycling extends the Arch-Router principle to the upcycling of dense models into MoE architectures by initializing multiple routers from pretrained attention heads (Ran et al., 31 Aug 2025). This methodology enables a collaborative, attention-like scheme for token-to-expert assignment:

Router Initialization: Each router $W^j$ is derived from the query transform of an attention head; expert keys $K^i$ are averages of attention keys from the dense checkpoint.
Multi-view Projections: For a token $x$ , $m$ queries $\{Q^j = W^j x\}_{j=1}^m$ are generated and paired with $n$ expert keys to yield a score matrix $S_i^j = \frac{(Q^j)^T K^i}{\sqrt{d'}}$ .
Score Aggregation: Final routing logits for each expert are $S_i = \sum_{j=1}^m S_i^j$ , normalized by softmax to yield routing probabilities $R$ .
Top- $k$ Assignment: Only the top scoring experts per token are selected.

Applied to Qwen 8×0.5B upcycled to eight experts and routers, this design yielded over 2 percentage point improvement versus vanilla (linear router) upcycling, with faster convergence, higher assignment diversity, and better expert specialization. Computational overhead remains modest compared to baseline. Attention-inspired collaborative routing thus directly addresses representation collapse and specialization barriers in upcycled MoE settings.

5. Parameterization, Scalability, and Practical Considerations

A unifying feature across all Arch-Router instantiations is explicit, modular parameterization:

NoCs (FPGAs): Number of ports, channel width, bRAM depth (supporting area/performance scaling and exploitation of local vs. global communication patterns) (Ezhumalai et al., 2010).
Preference-aligned LLMs: Route policy set and domain-action pairs (enabling seamless expansion and transparent user-defined routing) (Tran et al., 19 Jun 2025).
MoE Routers: Number and type of routers, dimensions of projections, auxiliary balancing losses, top- $k$ strategies (tailoring computational footprint, expressiveness, and routing determinism) (Harvey et al., 19 Jun 2025, Ran et al., 31 Aug 2025).

Scalability and power efficiency are critical design axes: in NoC routers, bandwidth scales linearly with port/channel count; in MoE systems, diversity in router projections and attention leads to improved expert utilization and specialization. Power and area efficiency are enhanced via modularity and judicious resource allocation. In all contexts, the parameterized architecture supports rapid prototyping and adaptation to diverse application demands.

6. Application Domains and Open-Source Availability

Arch-Router concepts have demonstrated practical utility in:

FPGA-based SoCs: Modular, synthesizable NoC frameworks for multicore design flows (via MoClib library) (Ezhumalai et al., 2010).
Conversational AI and Assistant Systems: Real-time routing of user queries to specialized LLMs with domain-action specificity—beneficial where latency and interpretability are critical (model and framework available at https://huggingface.co/katanemo/Arch-Router-1.5B and https://github.com/katanemo/archgw) (Tran et al., 19 Jun 2025).
MoE Model Scaling: Enabling efficient router replacement, fine-tuning, and upcycling in large transformer models (BERT, Qwen1.5-MoE, Qwen 8×0.5B) (Harvey et al., 19 Jun 2025, Ran et al., 31 Aug 2025).

7. Implications and Future Directions

The Arch-Router Framework represents a generalizable pattern for high-performance, flexible router design in both hardware and neural architectures. Direct implications include:

Improved throughput and area efficiency in reconfigurable system interconnects.
Highly accurate, preference-aligned, scalable routing for LLM ensembles with dynamic policy integration.
Robust, specialized, and diverse routing in MoE systems, including upcycled models, with minimal incremental cost.

Ongoing areas of investigation include deeper theoretical analysis of collaborative routing dynamics, extension to heterogeneous and multimodal settings, and the exploration of novel aggregation strategies in multi-router architectures. The framework’s adaptability and modularity make it a foundation for future research in efficient communication, scalable AI, and user-centric system design.

See also: Network-on-Chip (NoC), Mixture-of-Experts (MoE), FPGA multicore design, model upcycling, attention-based routers. Principal sources: (Ezhumalai et al., 2010, Tran et al., 19 Jun 2025, Harvey et al., 19 Jun 2025, Ran et al., 31 Aug 2025)