Dynamic Neural Workload Offloading
Reinforcement learning schedulers dynamically partitioning neural execution graphs between vehicle NPUs and 5G base station GPUs based on channel capacity.
Quantized Intermediate Tensor Compression
Entropy-coded 8-bit intermediate latent tensors reduce uplink transmission data volume by 78 percent without sacrificing perception bounding box mAP scores.
- Dynamic split-point selection based on live cellular channel capacity.
- Sub-20 millisecond end-to-end offloaded inference latency.
- 85% reduction in uplink wireless bandwidth payload.
- Automated local fallback if edge signal quality deteriorates.
Explore Domain Portfolio