Modular Decoder Architecture

• Modular Decoder Architecture is an organizational paradigm that decomposes the decoder into interchangeable, independently tunable submodules.
• It is applied in areas such as NLP, wireless communications, and quantum error correction to enable scalable, parallel, and adaptable decoding systems.
• Key benefits include plug-and-play module replacement, parameter isolation for specialized tuning, and improved throughput and resource efficiency.

A modular decoder architecture describes an organizational and implementation paradigm in which the decoder is partitioned into distinct, well-defined submodules that can be independently replaced, extended, or specialized. Modularity is a critical principle for system flexibility, extensibility, and resource optimization in both classical and quantum error-correction decoders and modern deep generative models. Across application domains—natural language processing, wireless communications, post-quantum cryptography, and quantum error correction—modular decoder architectures address scalability, code and modality flexibility, resource management, and the practical requirements of integrating heterogeneous subsystems.

1. Principles and Motivations for Modularity in Decoder Architectures

Modularity in decoder design is characterized by the decomposition of the decoding process into autonomous, reusable, and parameterizable components. In encoder-decoder models for conditional generative modeling (e.g., DiffuSIA (Tan et al., 2023)), this enables the independent updating, replacement, or extension of the encoder and decoder modules. For channel or source decoders (LDPC, polar, MDPC), modularity permits scalable hardware realizations, supports disparate code parameters (length, rate, kernel/order), and enables simultaneous multi-task processing.

Motivating factors for modular designs include:

• Flexibility to accommodate input and output modalities of varying structure (text, image, audio, etc.).
• Support for multiple code rates, block lengths, and kernel types, required by evolving communication and storage standards.
• Ability to scale decoding throughput and latency according to application requirements by adjusting parallelism or reusing hardware.
• Support for heterogeneous and dynamic quantum circuits, necessitating composable and reconfigurable decoding for fault-tolerant quantum computing.

2. Modular Decoder Architectures in Modern Sequence Generative Models

The modular encoder-decoder paradigm sits at the core of numerous deep generative models for conditional language modeling and multimodal generation.

DiffuSIA: Spiral Interaction Encoder-Decoder Text Diffusion

DiffuSIA (Tan et al., 2023) advances modularity by making the encoder and decoder fully detachable and independently designable:

• Encoder and Decoder Modules: The number of layers ($L_e, L_d$) and architectures can be independently selected and tuned to the task. Encoders and decoders can be swapped or extended without retraining the whole model stack.
• Plug-and-Play Extensibility: The architecture supports plugging in pre-trained, multimodal, multilingual, or domain-specific encoders and decoders, leveraging modular design for cross-domain transfer.
• Spiral Interaction Mechanism: A multilayer, spiral cross-attention fuses conditional (encoder) and generation (decoder) information across the stack. At each spiral step, the encoder attends to decoder state (producing a Target-aware Conditional, TaC), and the decoder attends back (Condition-aware Target, CaT):

$c^{m+1}_t = \mathop{\CACE}(c^m_t, x^n_t), \quad x_t^{n+1} = \mathop{\TransformerDecoder}(x^n_t, c^{m+1}_t)$

This structure enables mutual update and deep fusion, outperforming one-off cross-attention.

• Extensibility: Selectively replacing encoder or decoder modules supports multilingual, cross-modal, and task-specialized generative setups.

3. Hardware Modular Decoder Architectures for Channel and Source Coding

Multi-Mode LDPC and Polar Code Decoders

In high-throughput LDPC decoding for standards such as 5G NR (Pourjabar et al., 2021), modularity is achieved through:

• Partitionable Shift Networks: Shift networks are constructed as partitionable Banyan-type switching fabrics, supporting different lifting sizes ($Z$) by instantiating independent subnetworks and multiplexers.
• Multi-Block Parallelism: For small $Z$, hardware is reused by partitioning into independent blocks, decoding multiple frames in parallel.
• Modular Variable Node Types: Standard and extended VNs (EVNs) are separately implemented to address low-rate extensions, achieving code-unified support at resource efficiency.

In combinational multi-kernel polar decoders (Rezaei et al., 2022, Rezaei et al., 2023), modularity is reflected by:

• Recursive Decoder Composition: The SC decoder is constructed recursively from basic binary ($T_2$) and ternary ($T_3$) kernel modules, with combinational “glue logic” per stage, supporting arbitrary kernel sequences and block lengths $N = 2^n 3^m$.
• Online Rate Assignment: Modular input registers permit frozen/information set patterns to be updated at runtime.
• Python/VHDL Compiler Support: High-level compilers enable automated assembly of modular decoder architectures according to design specifications.

A summary of modular hardware decoder features is given below:

Feature	Implementation Aspect	Scalability/Modularity
Shift/Interconnect Network	Partitionable switch fabric	Supports arbitrary blocklength/parallelism
Kernel Blocks (Polar)	Recursive composition of $T_2, T_3$ modules	Supports arbitrary sequences and rates
VN Types (LDPC)	Separated Standard/EVN for extension	Efficient multi-rate, unified support
Task Assignment	Parametric instantiation/assignment of modules	Dynamically reconfigurable architecture

4. Modular Decoder Architectures in Quantum Error Correction

Quantum error-correction presents unique requirements; modularity is essential due to massive system sizes and the need for supporting dynamic, logical circuits.

LEGO and Edge-Vertex Modular Decoding

• Decoding Blocks: The LEGO architecture (Wu et al., 4 Oct 2024) introduces the decoding block as a unit encapsulating a decoding subgraph and its combination boundaries. Decoding blocks are pre-compiled, maintained in a library, and stitched together at runtime according to the logical circuit’s dynamic path. This abstraction enables run-time reconfiguration, parallel execution, composable resource management, and rapid adaptation to innovations in codes or hardware.
• Fusion-Based and Pipeline Parallelism: Specialized and general-purpose decoders act as modular computational resources allocated by a coordinator (OS scheduler analog). Block boundaries directly enable fusion-based parallelism and pipelined multi-stage decoding.
• Edge-Vertex Decomposition: For lattice surgery and topological codes (Bombín et al., 2023), the global decoding problem is decomposed into edge and vertex tasks. Edge tasks set boundaries first in parallel, vertices read these boundaries and decode locally, with decoupled data dependencies. Correct buffer sizing is essential for preserving the protocol’s fault distance.
• Resource-Efficient Partitioning: Distributed decoders (e.g., Helios (Liyanage et al., 20 Mar 2024)) instantiate per-vertex PEs on a grid reflecting code structure; resource-efficient context-switching (partitioned PE reuse) supports arbitrarily large code distances without redesign.

5. Modularity, Extensibility, and Performance

Modularity directly enables several system-level properties:

• Extensibility: Modular decoders allow straightforward extension/replacement (e.g., swapping a monolingual decoder for a multilingual one, or supporting new code families in hardware).
• Parameter Isolation and Specialization: Modules can be independently trained or tuned, supporting transfer learning, domain adaptation, or resource optimization without retraining the entire system.
• Plug-and-Play Assembly: Decoders with standard interfaces can be composed from independently developed or trained modules, as in LegoNN’s plug-and-play encoder/decoder composition (Dalmia et al., 2022).
• Parallelism and Scalability: Modular hardware blocks (PEs, kernel stages, memory banks) facilitate efficient mapping of parallel tasks, with clear scalability and resource-requirement metrics.

Performance data from the literature illustrate the architectural benefits:

Architecture	Peak Throughput	Scalability Mechanism	Area/Pwr Efficiency
LDPC 5G-NR (Pourjabar et al., 2021)	13.46 Gbps	Partitionable shift, multi-block	1.03 mm² / 229 mW @ 526 MHz
Polar MK (Rezaei et al., 2023)	1664.5 Mbps	Recursive modular combinational	Full support for $N=2^n3^m$
Helios QEC (Liyanage et al., 20 Mar 2024)	11.5 ns/round	PE grid, resource partitioning	Resource-latency trade-off

6. Modularity Across Domains and Future Directions

Modular decoder architectures are now standard in:

• Deep sequence and diffusion models: Modular encoder-decoder designs support multimodal, multilingual, and cross-task transfer and adaptation. Spiral and block-based interaction structures power deeper, context-rich fusion.
• Hardware error-correction decoders: Recursive, kernel-based, and block-parallel organizations ensure code- and rate-unified architectures, high throughput, and support for evolving code families.
• Quantum error-correction: Modular constructing of decoding blocks and partitioned execution (edge-vertex, block-parallel) are foundational for tractable real-time, resource-aware, and adaptable quantum decoding in large and dynamic circuits.

As quantum and classical systems further integrate, and as hardware constraints drive ever more heterogeneous and flexible communication and computation paradigms, modularity in decoder architectures is expected to become increasingly essential, enabling composability, code reuse, interoperability, and continuous upgradeability across diverse technological infrastructures.