NeuroTensor Labs
TECHNICAL WHITEPAPER & SYSTEMS ARCHITECTURE

GPU-Accelerated Spatial Computing
From Silicon to Real-Time Action

An engineering breakdown of how NeuroTensor Labs achieves deterministic sub-8ms inference loops on NVIDIA Jetson and H100 architectures via custom CUDA kernel dispatch, FP8 Tensor Core quantization, and Isaac Sim synthetic data pipelines.

End-to-End Glass-to-Action Execution Pipeline (Sub-6ms Total Cycle)
01

Sensor Ingestion

4x 4K RGB-D streams ingested via zero-copy pinned DMA memory.

0.4 ms Latency
02

CUDA Voxelization

Hand-tuned warp shuffle kernels convert point clouds to sparse 3D voxels.

0.9 ms Latency
03

Spatial Attention

FlashAttention-3 FP8 Tensor Core cross-attention across vision and geometry.

2.6 ms Latency
04

TensorRT Engine

Compiled execution plan executes fused feedforward layers with zero overhead.

1.3 ms Latency
05

Action Trajectory

6-DoF inverse kinematics vector dispatched directly to robot motor controllers.

0.6 ms Latency
Total End-to-End Cycle Time: 5.8 ms @ 128.4 FPS on NVIDIA Jetson AGX Orin 64GB
LOW-PRECISION COMPILATION

TensorRT 10 & FP8 SmoothQuant Compression

Traditional foundation models lose up to 6.2% accuracy under naïve INT8 quantization due to activation outliers in self-attention layers. NeuroTensor Labs implemented a proprietary SmoothQuant scaling technique designed specifically for NVIDIA Hopper (H100) and Blackwell Tensor Cores.

  • FP8 E4M3 & E5M2 Formats: Optimal dynamic range for spatial geometry coordinates and linguistic prompt embeddings.
  • 0.18% Perplexity Degradation: Virtually zero accuracy loss compared to FP16 floating-point baseline.
  • Zero-Copy Host-Device Transfer: Pinned system memory buffers bypass CPU memory copies entirely.
engine_builder.cu (CUDA 12.6 / TensorRT 10.0) NVIDIA COMPILED 
// Configure TensorRT 10 FP8 Builder for NeuroVoxel
auto config = builder->createBuilderConfig();
config->setFlag(BuilderFlag::kFP8);
config->setMemoryPoolLimit(MemoryPoolType::kWORKSPACE, 2ULL << 30);

// Custom Warp-Shuffle Spatial Kernel Dispatch
__global__ void voxelize_spatial_tokens_fp8(
    const __nv_fp8_e4m3* __restrict__ point_cloud_raw,
    __nv_fp8_e4m3* __restrict__ voxel_grid,
    const int num_points) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < num_points) {
        // Warp-level reduction across 3D voxels
        unsigned mask = __activemask();
        float3 coord = load_spatial_coords(point_cloud_raw, idx);
        int bin = compute_voxel_index(coord);
        atomicAddFP8(&voxel_grid[bin], 1.0f);
    }
}
SYNTHETIC DIGITAL TWIN SPECIFICATIONS

OmniSynthetic Data Pipeline

Real-world physical edge training data is scarce, expensive, and dangerous to collect for failure edge-cases. NeuroTensor Labs solves this by building an automated synthetic generation pipeline on NVIDIA Isaac Sim and NVIDIA Omniverse.

USD ASSET LIBRARY
85,000+
Photorealistic Industrial Assets
SIM-TO-REAL ACCURACY
98.2%
Domain Randomization Match

Randomized dynamics parameters: friction coefficients, lighting reflectance, sensor lens aberrations, and unexpected human pedestrian paths.

SYNTHETIC TWIN SIMULATION

Closing the Sim-to-Real Gap with NVIDIA Isaac Sim

By combining physically accurate ray tracing with RTX sensor simulation, we pre-train our models on over 10 Million synthetic scenarios before any code touches physical robotic hardware.

  • Automatic 3D bounding mesh & semantic segment ground-truth
  • Physics simulation powered by PhysX 5 GPU solver
  • Direct export to TensorRT calibration datasets
HARDWARE COMPATIBILITY MATRIX

Engineered for NVIDIA Silicon at Every Scale

Hardware Target Form Factor Precision Engine Latency Throughput Ideal Niche
Jetson AGX Orin 64GB Embedded Edge FP8 TensorRT 10 5.8 ms 128.4 FPS Warehouse AMRs & Drones
Jetson Orin Nano 8GB Ultra-Compact Edge INT4 AWQ Engine 9.2 ms 76.0 FPS Micro-drones & Smart Cameras
NVIDIA L40S Server Factory On-Prem Edge FP8 Triton Server 3.1 ms 280.0 FPS Sub-millimeter Inspection Lines
NVIDIA H100 SXM5 DGX Cloud Cluster Megatron FP8 Distributed 1.4 ms 640.0 FPS Foundation Pre-training (70B)

Ready to Benchmark on Your Proprietary Sensor Rig?

Download our open C++ benchmarks, evaluate sample TensorRT calibration profiles, or request custom engine compilation.

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