JetPack defines CUDA, TensorRT, and cuDNN behavior; the BSP sets kernel and driver capabilities those runtimes depend on. When these layers advance independently, library calls that expect newer driver primitives fall back to generic routines and TensorRT disables optimizations tied to missing capability flags. The result is lower throughput and higher latency even though inference completes.

We maintain a verified mapping between JetPack releases and BSP revisions, then rebuild kernel modules and device drivers within the current JetPack runtime to re-expose the correct accelerator features. During integration, we validate TensorRT and CUDA traces to ensure INT8 and FP16 precision kernels remain active, DLA offload is engaged where configured, and batch execution order matches the prior baseline.

JetPack releases also change how TensorRT selects tactics. A tactic that worked previously may exceed workspace limits or depend on updated driver hooks. We analyze each release to identify new dependencies and confirm the BSP provides the required support. When runtime behavior shifts, workspace sizing, precision fallback rules, or calibration cache format, we update deployment parameters so the model continues to use optimized execution paths. This synchronization restores the model’s intended execution plan without loss of throughput or precision.

Build environments determine how TensorRT and CUDA compile, link, and load optimized kernels. Even minor compiler or library differences can alter workspace allocation and kernel linkage, preventing the fastest kernels from loading and increasing end-to-end latency.

Our sustainment pipeline locks compilers, linkers, and libraries directly to BSP specifications. Builds run in containerized environments that mirror the deployment image, preserving ABI boundaries between binaries and the operating system. The RootFS is parameterized to update library paths and service definitions as vendors change directory structures, avoiding silent loader drift. We then verify runtime equivalence across hardware targets: identical models load with matching workspace usage and deliver the same throughput and latency characteristics. Performance changes are traced to explicit configuration choices, not environmental variance.

Kernel updates change driver interfaces and DMA/memory semantics that CUDA and TensorRT rely on. If drivers aren’t rebuilt under the new headers, capability flags drop, feature discovery breaks, and the runtime avoids fast kernels or moves work to the CPU. In sustainment, we rebuild GPU, DLA, and I/O drivers against the upgraded kernel and validate feature exposure through runtime profiling. We confirm concurrent compute/copy remains active, zero-copy paths are restored where expected, and allocator behavior matches the model’s workspace plan.

Power policy is tuned around the algorithm, not the board: frequency governors are calibrated so the model can maintain its validated batch size and precision without forced fallbacks or added copies. We judge success by kernel/precision selection, achieved batch size at the same workspace, sustained accelerator occupancy, and p50/p95 model latency, not by kernel scheduler metrics.

Sensor configuration defines the structure of model inputs. BSP or kernel updates that alter device-tree nodes, clock sources, bus routing, interrupt mapping, or metadata encoding, change the cadence and format of sensor outputs. The network still runs, but it sees inputs that no longer match the training distribution, and accuracy drops.

We audit each BSP update for sensor definition changes, rebuild the device trees, and validate driver outputs against the model’s expected schema. Validation measures frame cadence, timestamp alignment, and metadata integrity; if drivers relabel channels or change encoding, we update middleware pipelines so tensors constructed at runtime keep identical channel order, normalization, and sampling rate. For multi-sensor systems, we verify cross-sensor synchronization, measure inter-sensor skew, and adjust configuration to maintain sub-frame alignment. The failure mode here is accuracy loss from distribution shift; the fix is restoring input semantics so the model sees data equivalent to its training set.