SRC-Pipeline: Multi-Domain Modular Architecture

• SRC-Pipeline is a comprehensive framework that modularizes processes across software engineering, signal processing, computational neuroscience, and more.
• It employs techniques like vertical pipeline encapsulation, scene region compression, and cascaded parallel architectures to boost efficiency and reduce computational costs.
• Empirical benchmarks show order-of-magnitude improvements in release speed, FLOPs reduction, and accuracy across various high-impact applications.

The term "SRC-Pipeline" encompasses several distinct, high-impact paradigms across software engineering, signal processing, data reduction, computational neuroscience, physics, and machine learning. Presented below is a comprehensive, multi-domain account with emphasis on technically verifiable methodologies, architectures, formalisms, and application outcomes, as documented in foundational literature.

1. Self-Contained Cross-Cutting Pipeline Architecture (Software Engineering)

The Self-Contained Cross-Cutting Pipeline Architecture (SCPA)—sometimes referenced as the “SRC-Pipeline”—is a vertically decomposed software architectural paradigm designed to eliminate cross-layer dependencies endemic to traditional n-tier architectures (Patwardhan et al., 2016). Unlike classic horizontal layering (UI, Business Logic, Data Access, each with shared libraries), SCPA mandates that all logic for a discrete feature or bug fix is packaged within its own “vertical pipeline,” entirely encapsulating UI, BL, and DAL modules per feature.

Each pipeline fulfills a minimalist i-Plugin contract:

$$\mathrm{IPlugin} \equiv \{\,\mathrm{Load}: \mathrm{Context} \rightarrow \mathrm{void},\quad \mathrm{Execute}: \mathrm{Context} \rightarrow \mathrm{Context},\quad \mathrm{Next}: () \rightarrow \mathrm{IPlugin} \cup \{\mathrm{null}\} \,\}$$

Pipelines are discovered and linked at runtime via directory-based plug-in loading. No pipeline component is allowed to invoke or depend on another outside its assembly; global dependency graphs are thus collapsed into a disjoint collection of trees, minimizing change impact.

Quantitative benchmarks (5 projects, 15 months) found SCPA reduced release time by 42.99%, increased LOC delivered per cycle by 22.58%, and decreased post-release defect count by 85.54%. Rollback or switch-off is achieved atomically: removing the relevant DLL disables the feature with no system-wide retest, enabling rapid A/B, feature-flag, or emergency restoration—directly supporting agile and continuous deployment practices.

Best practices include strict single-responsibility pipeline boundaries, limited sharing of only truly global utilities (e.g., logging), pipeline-level unit and integration testing, and CI/CD systems that build and deploy pipelines independently (Patwardhan et al., 2016).

2. Scene Region Compression Pipeline (Vision-LLMs for Autonomous Driving)

The SRC-Pipeline in autonomous driving VQA accelerates large VLMs (e.g., Qwen2-VL) by compressing early video frames into low-rank “scene” and “region” tokens, retaining full spatial granularity only for the most recent frames (Cai et al., 11 Jan 2026). Formally, a sequence of video frames $(I_1, ..., I_T)$ yields patch sets $P_t$ via a vision transformer; for the first $M$ frames, dense patch tokens ($N$ per frame) are projected via a learnable transformer encoder $E_t$ into 1 scene + 4 region tokens with spatial masking:

$$\{\hat{S}, \hat{R}_1, ..., \hat{R}_4\} = f_{\text{compress}}(\{t_i\}_{i=1}^N)$$

This design reduces the effective token count and FLOPs budget by up to 66% without sacrificing VQA accuracy. E.g., on LingoQA, the full SRC-Pipeline achieves a Ling-Judge score of 57.28 with only one-third the compute cost of a baseline Qwen2-VL. Ablation shows that omitting region tokens, or using average pooling, significantly degrades performance.

The pipeline maintains positional and temporal encodings and can be grafted onto generic ViT-based architectures with minimal changes, offering a scalable pattern for latency-critical, real-time autonomous systems (Cai et al., 11 Jan 2026).

3. Wideband Sample Rate Converter: Cascaded Parallel-Serial Pipeline (Signal Processing)

In digital signal processing instrumentation, the wideband SRC-Pipeline implements cascaded, parallel-serial architectures to maximize throughput and flexibility in sample rate conversion (Ming et al., 2023). The front-end (“parallel” stage) demultiplexes the high-rate input into $L$ parallel lanes (e.g., 80 at 250 MS/s each), then applies a pipeline-parallel cascaded integrator-comb (CIC) stage and two halfband polyphase filters. The back-end (“serial” stage) provides further (arbitrary) decimation using industry-standard FPGA IP.

Parallelization transforms the standard recursive CIC (critical path $O(N)$ in adder stages) into an $O(\log L + \log N)$ pipeline using adder-matrix and adder-line constructs:

$$y(i, j) = \sum_{i'=0}^{i-1} I(i', L-1) + I(i, j)\,, \quad I(i, j) = \sum_{\ell=0}^j x(i, \ell)$$

This enables clock rates up to 400 MHz at 20 GS/s, with resource utilization below 7% on a Xilinx KU115 FPGA. Key performance results include input bandwidth up to 8 GHz, total decimation up to $\times 2.56$ million, and spectral alias suppression below –70 dB (Ming et al., 2023).

4. Data Reduction and Calibration Pipeline in High-Resolution Solar Telescopy

SRC-Pipeline, equivalently CRISPRED, provides a full-stack, modular, and validated data reduction chain for ground-based spectropolarimetry (e.g., the Swedish 1-m Solar Telescope) (Rodríguez et al., 2014). The pipeline links detector correction, Fabry–Pérot flat-fielding, polarization modulation/demodulation, camera co-alignment, multi-object multi-frame blind deconvolution (MOMFBD), and final 4D/5D cube assembly.

Key modules span:

• FPI transmission modeling and spatially resolved cavity/reflectivity error removal:

$$T(\delta\lambda, \delta R) = \frac{1}{1 + \frac{4(R + \delta R)}{(1 - R - \delta R)^2} \sin^2\!\bigl(\frac{\Psi(\delta\lambda)}{2}\bigr)}$$

• Polarimetric calibration via per-pixel Mueller matrices and telescope model inversion.
• Pinhole-array daily alignment calibrations achieving ≤0.02 px RMS camera registration, and residual cross-talk under $10^{-3}$ of $I$.
• MOMFBD image restoration in local isoplanatic subfields (default 35 Karhunen–Loève modes), plus spatial warping for sub-pixel spectral and spatial self-consistency.

Processing throughput is 2–4 hours wall time for a full $\sim$10 GB scan using 8 CPU cores. Resulting data cubes are science-ready for ingestion and show artifact-elimination and polarimetric stability (Rodríguez et al., 2014).

5. Sleep Replay Consolidation Pipeline in Equilibrium Propagation (Continual Learning)

The Sleep-Replay Consolidation (SRC) pipeline, as applied to Equilibrium Propagation (EP) in recurrent neural networks (RNNs), simulates sleep-like consolidation by replaying experience-driven spike patterns and updating synaptic weights via local STDP (Kubo et al., 12 Aug 2025). The pipeline consists of:

1. EP awake training: Alternating between free and weakly-clamped phases to find minima of augmented energy, yielding classic contrastive learning updates.
2. Sleep phase: Poisson-distributed input spikes are generated from historical input statistics, propagating through RNN dynamics:

$$v_i^l(t) \leftarrow v_i^l(t-1) + \eta_{\mathrm{rec}}\!\sum_j W_{ij}^l S_j^{l+1}(t-1) + \eta_{\mathrm{fb}}\!\sum_k (W^l)_{ik}^T S_k^{l-1}(t-1)$$

with thresholding and reset for spike emission. Synaptic modifications occur via:

$$W_{ij}^l \leftarrow W_{ij}^l + \begin{cases} +\Delta_{+}, & S_i^l(t)=1 \land S_j^{l+1}(t-1)=1 \ -\Delta_{-}, & S_i^l(t)=1 \land S_j^{l+1}(t-1)=0 \ 0, & \text{otherwise} \end{cases}$$

Amplifies old memory traces independent of the new task stream.

1. Integration with awake replay: A rehearsal buffer maintains a small sample of previous data for supervised interleaving during awake EP training.

Empirically, this SRC pipeline yields up to 50% gains in class-incremental test accuracy across MNIST, Fashion MNIST, KMNIST, CIFAR-10, and ImageNet-10 sequential task regimes, outperforming or equaling BPTT-trained RNNs and advanced regularization schemes (Kubo et al., 12 Aug 2025).

6. SRC Pipelines in Nuclear Physics and Bot Detection

In nuclear physics, the SRC-pipeline denotes a computational procedure for counting two- and three-nucleon short-range correlated (SRC) clusters from the shell-model ground state (Shlush et al., 16 Dec 2025). The method formally projects pairs/triplets within a given spatial cutoff, evaluates contributions using harmonic oscillator wavefunctions, and produces normalized abundances:

$$N_{pn}^{(2)} = \sum_{i<j} \langle \Psi_0 | \hat{P}^{(2)}_{pn}(r_{ij}<r_0) | \Psi_0 \rangle$$

Resulting model predictions benchmark 2N- and 3N-SRC abundances for Al, Fe, Pb, Ca isotopes, providing cross-normalized ratios with respect to carbon and identifying a reference baseline for three-nucleon clusters ($R_{3/2} \approx 2.5\%$ for medium/heavy nuclei).

In network security, the “SRC pipeline” refers to the series of transformations from multi-modal behavior vector extraction to Spearman rank correlation analysis for bot detection (Al-Hammadi et al., 2010). Hooked API events are processed into signals, normalized and time-binned, with detection strength reported as a function of rank correlation:

$\rho = 1 - \frac{6 \sum_{i=1}^n d_i^2}{n(n^2 - 1)}$

Threshold-based classification is then applied for robust, real-time detection with low overhead.

7. Comparative Perspectives, Adoption Patterns, and Limitations

Across all domains, the SRC-Pipeline unifies the principle of information compression, modularization, or replay along physically, functionally, or temporally coherent axes. In software architectures, vertical segmentation streamlines CI/CD; in hardware and signal processing, pipeline parallelism achieves maximal throughput per resource; in deep learning and neuroscience, consolidation or compression minimizes interference and computational cost.

Nonetheless, specificity of encapsulation (software), compression (VQA), and statistical replay (neuroscience) must be carefully tuned: over-large pipelines, simplistic compression (e.g., average pooling), or nascent replay schemes may substantially degrade performance compared to the optimal variant. In network security, SRC-based correlation is limited to detecting monotonic relationships and suffers blind spots for events (pure flooding) lacking key-log signatures.

Empirical evidence and theoretical analyses demonstrate that SRC-Pipelines consistently deliver order-of-magnitude gains in modularity, efficiency, or stability in their respective contexts. Further extensions are generally regarded as promising, including pipeline adaptation to longer video horizons in VQA, multi-branch shell-model correlation in nuclear physics, and adaptive buffer prioritization in continual learning pipelines (Patwardhan et al., 2016, Cai et al., 11 Jan 2026, Ming et al., 2023, Rodríguez et al., 2014, Kubo et al., 12 Aug 2025, Shlush et al., 16 Dec 2025).