Unified Media Pipelines: Bridging Edge SDKs to High-Performance Hardware
ZetaNova Labs unifies heterogeneous engines—including Google AI Edge, NVIDIA DeepStream, and ONNX Runtime—into a single zero-copy C++ processing pipeline scaled seamlessly across client browser WASM and Grace-Blackwell enterprise clusters.
Custom Edge Vision Solutions Built for Zero-Latency Local Subdeck: We engineer tailored computer vision modules using hardware-accelerated, zero-copy pipelines designed for your exact hardware and performance needs.
Detects objects in video files, live camera feeds, or high-throughput RTSP streams with low latency.
21 3D hand landmarks estimated in real-time. Designed for touchless UI control, sign language interpretation, and spatial AR interaction.
Sub-millimeter facial feature extraction with 478 3D mesh points for attentiveness monitoring, biometric authentication, and live AR filtering.
Multi-category foreground extraction and background blur/replacement directly inside the live video stream without cloud dependency.
Real-time 33 3D body-landmark extraction for motion capture, workplace safety/ergonomics scoring, and athletic movement analytics.
Hardware-accelerated decoding, AI inference overlay, and sub-100ms browser video streaming with zero memory copies between GPU and CPU.
Cross-camera persistent tracking, heatmapping, line-crossing counting, and intrusion detection across complex facility camera grids.
Edge-deployed unsupervised defect detection and surface anomaly scoring for continuous, high-speed manufacturing lines.
Optimized zero-copy C++ pipelines for high-throughput enterprise hardware.
By maintaining video frame buffers strictly inside GPU Device VRAM (nvbufsurface), our pure zero-copy C++ architecture eliminates host memory staging across all connected SDK runtimes.
| Pipeline Stage | Legacy Staging Architecture | ZetaNova Labs Zero-Copy C++ Engine |
|---|---|---|
| Ingest & Decode | CPU software (FFmpeg libavcodec) into Host RAM | Direct NVDEC hardware ingest via Video Codec SDK |
| Memory Staging | Explicit PCIe bus copy (cudaMemcpyHostToDevice) | Strictly GPU VRAM (nvbufsurface) via NVLink-C2C |
| Inference Engine | Software frame buffering on standard FP32/FP16 models | TensorRT INT8/FP4 quantized engines / NIM Microservices |
By running decode and inference within a single GPU's device VRAM, our zero-copy C++ architecture processes dozens of concurrent camera streams without staging a single frame to host memory using Ada Lovelace's parallel hardware decode engines to hold stream density where legacy pipelines fall apart.
| Pipeline Stage | Legacy Staging Architecture | ZetaNova Labs Zero-Copy C++ Engine |
|---|---|---|
| Ingest & Decode | CPU software (FFmpeg libavcodec) decoding streams sequentially into Host RAM | Parallel hardware ingest via 4x NVDEC engines, decoding directly into GPU VRAM |
| Memory Staging | Explicit per-stream PCIe bus copy (cudaMemcpyHostToDevice) for every frame | Frames remain strictly in GPU VRAM (nvbufsurface) from decode through inference—zero PCIe transfer |
| Batching & Inference | Sequential per-stream inference on unbatched, software-buffered frames | Multi-stream batched TensorRT INT8/FP16 engines, sharing one GPU's memory pool across all active feeds |
Fine-tune pre-trained vision models securely using proprietary datasets.
Upload custom image archives, video RTSP feeds, or sample metadata telemetry packets directly through secure local buffers.
Execute modular training tasks via built-in framework wrappers to shape custom weights, gesture categories, and bounding labels.
Package trained outputs into production-ready .tflite flatbuffers, ONNX graphs, or TensorRT engine artifacts.
ZetaNova Labs is a specialized technology firm dedicated to developing real-time computer vision applications, high-throughput video pipelines, and edge-native software.
We bring decades of expertise to the entire digital video lifecycle—from capture, filtering, and segmentation to real-time detection, encoding, analytics, and low-latency playback.
Our team builds custom computer vision solutions for edge systems and embedded enterprise platforms running on local NVIDIA GPUs. Whether engineering C++ pipelines with DeepStream or scaling high-density inference across NVIDIA Grace-Blackwell clusters, our target is maximum hardware efficiency with zero latency overhead.
Let's build hardware-accelerated solutions together. Reach out to discuss your requirements and deploy high-performance NVIDIA GPU pipelines.