System Architecture
Silicon-Level Intelligence
Core
Methodology
Modern neural networks are often designed for unconstrained cloud environments. PlanLife re-engineers these architectures for the physical limits of mobile hardware, prioritizing deterministic execution and thermal stability.
Feasibility AuditArchitectural
Sparsity
Our pruning workflows identify and remove redundant parameters that contribute nothing to inference accuracy. We go beyond simple magnitude pruning, analyzing individual layer sensitivity to ensure the network remains robust under high compression ratios.
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01
Structured Pruning
Removing entire filters and channels to directly align with the SIMD lane widths of modern NPUs.
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02
Unstructured Sparsity
Weight-level zeroing for specific kernels, optimizing memory bandwidth on specialized sparse hardware.
// METHODOLOGY_NOTE: Layer-Wise Profiling determines the exact sparsity threshold for each block, preventing the accuracy collapse common in automated black-box tools.
We strip away
the abstraction.
INT8 & Mixed
Precision Math
Cloud models often reside in FP32 (32-bit floating point). We compress these into 8-bit integers through Quantization-Aware Training (QAT), utilizing on-device hardware engines to accelerate inference by up to 4x while maintaining original accuracy ranges.
Latency Thresholds
By strictly adhering to mobile instruction sets, we ensure every operation is executed directly by the NPU, bypassing the slow CPU/GPU handoffs that plague unoptimized models.
System Ready
Integer Optimization
Converting neural weights to INT8 format for massive reductions in memory bandwidth usage and thermal output.
FP16 Mixed Precision
Selective precision scaling for sensitive layers (like softmax or attention heads) to protect model convergence.
Dynamic Range Scaling
Per-channel scaling factors calculated during validation to minimize rounding errors in deep transformers.
Kernel Fusion
Combining activation functions directly into convolution layers to save wasted memory cycles.
Validation Hierarchy
Rigorous stress testing is the final gate before deployment. We don't just optimize; we verify every frame of performance.
We perform bit-for-bit comparisons between the original FP32 model and the optimized INT8 version. Our benchmark is a maximum accuracy drop of <1% across standard validation sets.
Models are tested on raw mobile physical hardware (not simulators). we measure inference time across repeated cycles to identify jitter and worst-case latency tail-ends.
Optimization isn't just about speed; it's about energy. We profile power draw and heat generation to ensure your AI works without triggering hardware throttling in real-world use.
Validation Protocol: Active
Start your technical
review.
Send us your model architecture in ONNX or TFLite format for initial layer profiling.
Define your target mobile hardware and performance benchmarks (latency, memory, etc.).
