ASIL-D compliant quantization achieving 87% model compression while maintaining 99.2%+ accuracy for autonomous vehicle deployment on Tesla HW3/HW4 and next-generation platforms.
Systematic precision reduction with validation gates at each stage ensuring safety-critical accuracy preservation throughout the compression process.
TEQ provides quantization-specific implementations that solve the unique computational challenges of deploying safety-critical AI to resource-constrained automotive platforms.
Mixed-precision quantization that identifies and preserves higher precision for safety-critical layers while aggressively quantizing non-critical layers, achieving optimal accuracy-memory tradeoff.
Computes safety gradient magnitude for each layer. Higher gradients indicate higher safety criticality requiring precision preservation.
FP16 for critical layers (≥0.8 score)
Attention Q/K/V: FP16 (0.91 criticality)
Halting probability: FP16 (0.97 criticality)
Expert feed-forward: INT4 (0.35 criticality)
SCLPP achieves 97.8% edge case accuracy with marginal memory increase by preserving precision exactly where it matters for safety.
1:1 memory-accuracy tradeJoint quantization of ensemble models to preserve inter-model independence properties critical for safety consensus. Prevents quantization-induced artificial correlation between Tier A and Tier E models.
Standard quantization increases inter-model correlation by 87%. CPQ limits correlation increase to just 13% through scale perturbation techniques.
6.7× better than standard INT8Maintains effective voter count (n_eff) at 10.1 from 10.4 baseline, compared to 8.2 with standard quantization—a 7× improvement in ensemble integrity.
n_eff: 10.1 (vs 8.2 standard)Cross-Architecture Independence Index drops only 2% (0.89→0.87) vs. 15% degradation with independent quantization.
CAII Δ: 2% vs 15%Specialized QAT extension for PonderNet's adaptive halting mechanism, preserving reasoning depth correlation under quantization through halting probability loss and depth correlation constraints.
KL divergence between quantized and FP32 halting probabilities ensures pondering behavior remains consistent after precision reduction.
97% halting step correlationCorrelation loss between quantized and FP32 expected depth preserves adaptive computation patterns for complex scenarios.
87% depth MSE reductionPQAT achieves 96.8% edge case accuracy vs. 92.4% with standard PTQ—critical for construction zones, adverse weather, and unusual obstacles.
+4.8% edge case accuracyConstructs calibration datasets with systematic oversampling of safety-critical scenarios, ensuring quantization accuracy where it matters most for autonomous vehicle perception.
Before: Edge cases 1%, Adverse weather 7%
After: Edge cases 15%, Adverse weather 20%
Ensures rare but critical scenarios calibrate quantization ranges.
Fog accuracy improves from 89.2% to 95.8%—critical for preventing the 9% error rate that caused 1 incorrect decision every 41 seconds in fog conditions.
+7.4% fog accuracyConstruction accuracy improves from 91.1% to 96.2%, preventing premature halting in PonderNet when encountering faded lane markings.
+5.6% construction accuracyRuntime precision configuration based on detected hardware capabilities, enabling single model deployment across Tesla HW2.5, HW3, HW4, and future HW5 platforms.
Single quantized model adapts to hardware: INT8-only for HW2.5/HW3, mixed INT4/INT8/FP16 for HW4, full precision flexibility for HW5+.
4× platform coveragePrecision-parametric bounds enable single safety certification covering all hardware variants, eliminating 4× validation overhead.
75% validation cost reductionSingle model binary for all hardware generations simplifies over-the-air updates across heterogeneous fleet.
Single binary deploymentReal-time monitoring for quantization numerical instability with fail-safe triggering before safety-critical errors occur, extending autonomous safety framework with quantization-specific detection.
Monitors INT8 accumulator saturation in real-time. Triggers precision upshift when overflow rate exceeds 0.01% threshold.
<0.1% overflow rateTracks decision confidence distribution shift indicating quantization degradation under adverse conditions.
99.99%+ decision confidenceGraceful degradation to higher precision (INT8→FP16) or driver handoff when numerical instability detected.
Pre-emptive safety triggeringProduction-validated metrics demonstrating TEQ's ability to deploy full VectorCertain ensemble on resource-constrained automotive embedded platforms.
FP32→INT8 compression achieving <4 MiB SRAM footprint for complete Tier E inference
Maintains 96.5% overall accuracy from 97.3% baseline with SCLPP mixed-precision
Deterministic worst-case execution time on Tesla HW3 NPU architecture
Cross-Architecture Independence Index drops only 2% vs. 15% with standard quantization
Edge case accuracy in fog, rain, and snow conditions with SSCDE calibration
Complete ISO 26262 validation framework for automotive functional safety
Single quantized model automatically configures optimal precision for each Tesla hardware generation through runtime capability detection.
VectorCertain + TEQ quantization enables each Tesla hardware generation to achieve higher ASIL certification levels than previously possible.
The only quantization solution with complete automotive functional safety validation for life-critical autonomous vehicle deployment.
Validated on historical autonomous vehicle incident scenarios to ensure quantized models prevent catastrophic failures in the most critical edge cases.
Full-precision VectorCertain detected radar/camera disagreement 1.7 seconds before impact. INT8 quantized models preserve the 0.89 Tier E confidence triggering emergency brake.
PQAT preserves full 12-step pondering depth for ambiguous construction zones. Standard INT8 caused premature halting at step 6—now validated to maintain correct "uncertain lane" detection.
SSCDE calibration improves fog scenario accuracy from 89.2% to 95.8%, eliminating the 1-in-11 incorrect detection rate that caused unsafe decisions every 41 seconds.
Combined PQAT + SSCDE ensures proper pondering depth and calibration for temporary barriers, cone patterns, and lane shifts. Accuracy improves from 91.1% to 96.2%.
Multi-sensor consensus preserved under quantization for siren detection, flashing lights, and vehicle trajectory prediction. CPQ maintains cross-architecture independence.
VRU (Vulnerable Road User) detection maintains highest accuracy priority under SCLPP. Attention layers preserved at FP16 for pedestrian tracking.
Standard quantization treats models as black boxes—TEQ understands ensemble safety semantics.
Autonomous vehicle AI requires three properties that standard quantization destroys:
TEQ treats quantization as a safety engineering problem, not just compression. Each innovation (CPQ, PQAT, SCLPP, SSCDE, HAPS, QADD) addresses a specific failure mode that would otherwise make quantized VectorCertain unsafe for autonomous deployment.
TEQ enables HW2→ASIL-C, HW3→ASIL-D, HW4→ASIL-D+ certification upgrades—unlocking Level 4 autonomy on 6M+ existing vehicles.