BKAM Architecture — Attention Dynamics
Bi-directional Kinematic Attention Modeling
BKAM projects multi-sensory observation vectors into continuous kinematic coordinates, aligning inputs from asynchronous sensors at low processing overhead.
Inference Processing Architecture
Modular Intelligence Blocks
Spatial Encoder
Converts raw sensor coordinates into unified kinematic state vectors. Supports heterogeneous input dimensions.
Temporal Aligner
Synchronizes asynchronous sensor streams using adaptive clock interpolation. Compensates for variable sampling rates.
Attention Kernel
Computes bi-directional attention scores using kinematic distance functions. O(N) memory complexity.
Trajectory Predictor
Generates predictive state estimates for occluded or delayed observations. Failsafe extrapolation bounds.
Target Execution Profile
Design-target execution profile for edge deployment under a strict 15W TDP ceiling.
| STREAM DATA | FREQUENCY | TARGET LATENCY |
|---|---|---|
| CAMERA_RGB_ARRAY | 120 Hz | 1.42 ms |
| LiDAR_POINT_CLOUD | 60 Hz | 1.85 ms |
| RADAR_DOPPLER_STREAM | 100 Hz | 0.91 ms |
| BKAM_FUSION_CORE | 120 Hz | 1.94 ms |
Zero-Trust Inference Architecture
All inference operations execute within hardware-bound security enclaves. No raw sensor data leaves the device boundary — ever.
Inference confined to hardware-bound trusted execution environments.
No data leaves the device perimeter. All telemetry is local-only.
Model binaries compiled on-device with architecture-specific optimizations.
Weights quantized to INT8 precision for minimal memory footprint.
Process memory isolated via hardware-enforced page table boundaries.