A patent-pending architectural class built on Cross-Scale Fusion. Not an optimization of existing models — a new architectural foundation.
Today's deep learning ecosystem is trapped between two structurally broken extremes.
Traditional convolutional architectures aggressively downsample at every layer, destroying nearly 80% of spatial information before deeper layers ever see it. Once detail is discarded, subsequent modules can only rely on statistical approximation to compensate. This makes systems inherently fragile against real-world noise, distortions, and environmental fluctuations.
Global attention mechanisms attempt to recover the loss incurred by CNNs by establishing relationships across all data points. However, this approach increases computational complexity exponentially (quadratic scaling). This architectural heaviness creates significant bottlenecks in real-time pipelines and on hardware with limited memory bandwidth.
The result: Models are either too blind to be safe, or too heavy to be fast. The industry calls this a trade-off. We call it the Efficiency Wall — and we built CSF Core to break it.
Three engineering pillars that address the structural limitations of every existing architecture.
Unlike traditional models that isolate spatial details in early layers, CSF Core employs long-range skip connections to inject high-resolution, fine-grained structural data directly into deep layers. This ensures that spatial fidelity is never sacrificed for semantic context. The raw data injection path sends high-resolution spatial cues directly into deeper layers, avoiding redundant heavy blocks and preventing early-stage information loss. Along the curved fusion path, coordinate fidelity is maintained by precisely merging "where" and "what" features — eliminating the localization blur that legacy backbones cannot avoid.
CSF Core moves beyond rigid, sequential architectures. Its DAG-based structure allows information to flow through multiple pathways and scales simultaneously. This topology stabilises gradient flow, accelerates training convergence, and provides the system with intrinsic robustness against external noise and environmental distortions. Because the flow is concurrent, it naturally runs more efficiently on parallel silicon — but the primary impact is information preservation that legacy backbones simply cannot achieve.
By integrating Atrous Spatial Pyramid Pooling directly into the core fusion engine, the architecture captures multi-scale relationships without the quadratic computational overhead associated with Transformer-based attention. CSF Core is also engineered to minimise data movement — the primary bottleneck in modern computing. By optimising scale-wise information pathways, it maximises memory bandwidth efficiency, resulting in superior throughput on GPUs and radical energy efficiency on power-constrained embedded systems.
Validated against DINOv2 (Transformer) and YOLOv8-S (CNN) on ImageNet-1K and ImageNet-C.
| Architecture | Family | Parameters | GFLOPs | Top-1 Accuracy | Robustness (mCE) | Throughput |
|---|---|---|---|---|---|---|
| CSF Core (MYELION) | DAG / Fusion-First | ~61M (↓ 80% vs DINOv2) | ↓ 75% vs DINOv2 | 81.0% | −8.5 mCE | 2.4× vs DINOv2 |
| DINOv2 | Transformer / ViT | ~307M | High | ~80.8% | Baseline | 1× |
| YOLOv8-S | CNN | ~11M | 28.6 | 77.5% | Lower | Varies |
ImageNet-1K Top-1 · ImageNet-C robustness · Raw PyTorch throughput on identical hardware
CSF Core maintains a consistent efficiency profile across the entire hardware stack — no hardware-specific tuning required.
The topology saturates parallel compute without any hardware-specific tuning. Scales seamlessly from low-power edge to high-end data-center accelerators.
CSF Core began with vision. The DAG topology generalises naturally to spatial, temporal, and signal-based domains.
Image classification, object detection, semantic segmentation. The original domain — fully validated.
3D point cloud processing for autonomous systems. Structural fusion advantage translates directly to spatial data.
Signal-based environmental sensing. Low-compute requirements make CSF Core ideal for radar-heavy edge deployments.
Temporal signal processing. The DAG topology's concurrent pathways apply to sequential audio feature extraction.