Autonomous Driving Thermal Management for Multi-GPU Inference Workloads

Overview of Technical Issues:

Under multi-GPU inference workloads in autonomous driving systems, the cooling structures provide insufficient heat removal capacity during peak computational demands, causing GPU temperatures to exceed thermal limits and triggering performance throttling; the goal is to maintain stable GPU operating temperatures and sustained inference performance across varying workload intensities and ambient conditions.

Solution directions generated for this problem

Problem Direction 1 :

ImproveHeat dissipation capacity
VS
ConstraintCooling system volume

Inspiration 1 : Cross-domain reference

Application Principle: #7 Nested doll (Nesting)
Cross-domain applicability Assess applicability
Multiphase heat dissipation devices for electronic devices
Innovative Solution Refine solution

Nested micro-channel cold plate with embedded vapor chamber core

Embed vapor chamber inside cold plate structure
How to solve :
  • Nest a flat vapor chamber (0.6mm thick, copper-water, ≥200 W/(m·K) effective conductivity) directly between GPU contact surface and micro-channel layer, replacing 3mm solid copper base — reduces thermal resistance by 0.15 K/W while adding <2% volume
  • Integrate sintered copper wick (porosity 60%, pore size 50μm) on vapor chamber evaporator side for capillary-driven phase-change heat spreading across 15cm² GPU area, enabling 1200W peak dissipation within original 15cm×10cm×2.5cm cold plate envelope
  • Machine 0.4mm×0.8mm micro-channels (hydraulic diameter 0.53mm, 80 parallel channels) directly into vapor chamber condenser surface, achieving 6 L/min flow at 25kPa pressure drop — total heat transfer coefficient improves from 8,000 to 13,500 W/(m²·K)
Expected Effect : Heat dissipation capacity +50% (800W→1200W), volume increase <5%, GPU temperature reduced 18°C at 1000W load, thermal resistance <0.25 K/W
Risk Control :
  • vapor chamber vacuum seal integrity (leak rate must be <10⁻⁸ Pa·m³/s, helium mass spectrometry test required)
  • wick-to-wall bonding strength (shear strength ≥15 MPa, ultrasonic inspection for delamination)
  • micro-channel machining tolerance (width ±0.02mm, depth ±0.03mm, optical profilometry verification)

Problem Direction 2 :

ImproveHeat dissipation capacity
VS
ConstraintPump power consumption

Inspiration 1 : Cross-domain reference

Application Principle: #19 Periodic action
Cross-domain applicability Assess applicability
A motor control system based on OR gates
Innovative Solution Refine solution

Pulsed coolant flow with thermal capacitance buffering for GPU cooling

Implement pulsed flow cooling synchronized with GPU workload cycles
How to solve :
  • Install variable-frequency pump controller (PWM 0.5–5 Hz) driven by GPU temperature feedback: trigger 8 L/min bursts for 10-second intervals when T>82°C, drop to 3 L/min baseline when T<78°C, achieving 18–22W average power vs. continuous 45W
  • Integrate 500ml coolant reservoir with high thermal capacitance (water + 20 wt% ethylene glycol, specific heat 3.8 kJ/kg·K) to absorb transient 1000W spikes during low-flow phases, storing 45 kJ thermal energy over 60-second inference peaks
  • Deploy real-time workload predictor using autonomous driving task scheduler: pre-trigger pump ramp-up 3 seconds before known inference bursts (intersection detection, sensor fusion), reducing reactive temperature overshoot from 20°C to <8°C while maintaining 20W average pump power.
Expected Effect : Average pump power 20W (−56% vs. continuous high flow); GPU temperature swing ±7°C (target ±5°C); peak dissipation 1050W; system efficiency +12%
Risk Control :
  • PWM controller reliability under thermal cycling
  • coolant reservoir leakage risk
  • workload prediction accuracy <85%

Problem Direction 3 :

ImproveCoolant flow rate
VS
ConstraintPump power consumption

Inspiration 1 : Cross-domain reference

Application Principle: #15 Dynamics
Cross-domain applicability Assess applicability
Warm floor data center
Innovative Solution Refine solution

Variable-geometry micro-channel cold plate with adaptive flow resistance

Adaptive flow path geometry reduces resistance during peak loads
How to solve :
  • Install shape-memory alloy (SMA) vanes at micro-channel inlets that expand from 0.6mm to 1.2mm width when GPU temperature exceeds 80°C, reducing hydraulic resistance by 60% and enabling 7 L/min flow at only 22W pump power
  • Use nickel-titanium SMA strips (transformation temperature 78-82°C, response time <3 seconds) embedded in channel walls, actuated purely by coolant temperature without external power
  • During idle periods (GPU <75°C), vanes contract to 0.6mm, maintaining 4 L/min baseline flow at 15W, then autonomously widen during inference peaks to achieve 7 L/min flow with only 22W pump power instead of the 45W required for fixed-geometry channels
Expected Effect : Flow rate +75% with pump power +47% (vs +200% baseline); temperature swing reduced to ±8°C; zero actuation energy
Risk Control :
  • SMA fatigue after 10^5 thermal cycles
  • vane position calibration drift
  • coolant contamination blocking vane movement

Problem Direction 4 :

ImproveThermal interface conductance
VS
ConstraintCooling system volume

Inspiration 1 : Cross-domain reference

Application Principle: #40 Composite materials
Cross-domain applicability Assess applicability
Heat dissipation assembly for phased-array antenna TR heat management and manufacturing method thereof
Innovative Solution Refine solution

Graphene-silver composite TIM with micro-pillar interface for zero-volume thermal upgrade

Replace existing TIM with graphene-silver composite
How to solve :
  • Formulate graphene-silver composite TIM: 60 wt% silver flakes (3–5 μm), 15 wt% graphene nanoplatelets (5–10 layers, lateral size 10 μm), 25 wt% silicone matrix
  • achieve thermal conductivity ≥200 W/(m·K) and interface resistance 0.12–0.15 K·cm²/W at 0.15 mm thickness
  • Apply via stencil printing with 150 μm aperture mask, dispense 0.6 g/cm² paste, cure at 120°C for 30 min under 50 kPa compression to form 0.15 mm bondline — 70% thinner than baseline 0.5 mm layer, reclaiming 0.35 mm vertical clearance per GPU
  • Integrate copper micro-pillar array (50 μm diameter, 100 μm height, 200 μm pitch) electroplated onto cold plate base
  • pillars penetrate TIM layer to create direct metal-to-die contact zones covering 15–25% of interface area, reducing effective resistance to 0.08–0.10 K·cm²/W while maintaining 0.15 mm total interface thickness
Expected Effect : Interface resistance reduced to 0.10 K·cm²/W (67% improvement); heat transfer efficiency +38%; vertical space saved 0.35 mm per GPU; GPU junction temperature reduced 12–15°C at 1000W load
Risk Control :
  • graphene dispersion uniformity in matrix
  • micro-pillar coplanarity tolerance ±10 μm
  • TIM pump-out under thermal cycling

Problem Direction 5 :

ImproveTemperature stability
VS
ConstraintMust not deteriorate

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Silicone composition crosslinking catalysts
Innovative Solution Refine solution

Predictive thermal pre-conditioning system with workload-synchronized cooling

Pre-cool GPU before predicted inference peaks using driving task scheduler
How to solve :
  • Integrate autonomous driving task scheduler with thermal controller to trigger pump ramp-up 8 seconds before known inference bursts (intersection approach, pedestrian detection clusters)
  • pre-cool GPU from 82°C baseline to 72°C target using 7 L/min flow
  • Embed 150g paraffin wax PCM (melting point 76–78°C, latent heat ≥200 kJ/kg) in cold plate channels to absorb first 45 kJ of spike heat at constant temperature, buffering 30-second transients without temperature swing
  • Implement three-stage flow control: 7 L/min pre-cooling (8 sec before peak), 5 L/min during PCM buffering (30 sec), 4 L/min baseline (idle) — average pump power 24W vs 45W continuous high flow, temperature variation reduced to ±6°C
Expected Effect : Temperature swing 30°C→±6°C; pump power 45W→24W; no throttling events
Risk Control :
  • task prediction accuracy <85% causes mistimed pre-cooling
  • PCM encapsulation leakage under thermal cycling
  • flow transition lag during rapid workload changes
Patsnap Eureka Solution