Thermal Challenges of High-Performance Embedded AI Modules

September 15, 2025

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Thermal Challenges of High-Performance Embedded AI Modules

The data center is no longer the only place where the AI revolution is occurring. High-performance embedded AI modules are enabling trillions of operations per second in form factors tiny enough to carry in your hand, from edge vision systems and autonomous drones to industrial robots, traffic management systems, weather prediction, the gaming console market, and medical imaging equipment. However, managing the heat is an equally challenging task.

Embedded AI modules function in small, tightly sealed enclosures, in contrast to server GPUs, which have massive heatsinks and refrigerated aisles [1]. One of the main design limitations nowadays is thermal performance, which has an impact on form factor, acoustics, sustained performance, and dependability.

Figure 1: Embedded AI peripherals deployed at the edge.

Key Factors Contributing to Heat Generation

Parallel Processing

AI on GPUs is different than conventional CPUs. GPUs contain a considerable number of small cores in comparison to a CPU, which has 8 or 16 cores. These small cores manage multiple threads simultaneously, known as parallel processing. For example, think of it like small kayaks, each transporting one person, in comparison to a medium-sized ship. AI workloads need to process tera operations per second, so they need thousands of tiny cores to execute instructions in a short succession of time. This intense calculation conducted by transistors produces heat and raises the junction temperature of the die [2].

Junction temperature follows the equation below:

Tj = Ta + Q x (Rθ)

• Tj = allowable junction temperature
• Ta = ambient temperature
• Q = dissipated power (watts)
• Rθ = thermal resistances in °C/W = Rjunction−to−case​+Rcase−to−sink​+Rsink−to−ambient​ [3].

The thermal resistance of the heatsink is the most crucial factor for the embedded AI module. For example, a GPU heatsink consuming 100W with 0.3 °C/W thermal resistance will be 30 °C higher than the ambient temperature. The allowable junction temperature is around 85 to 100°C for most GPUs, and thermal engineers, along with heatsink manufacturers, design the die and case to be 10 to 15 °C lower.

Dynamic switching per pin in HBMs

Power dissipation in a 1024-bit HBM2 (high bandwidth memory) bus follows the equation below [4]. Idle power is determined by leakage current, and power consumed while the memory banks that switch between read and write cycles are additional sources of power dissipation.

P = N *Cload * V2 * f = 3.73 W

Cload = ~0.5 – 1.0pF = capacitance per pin

V = Voltage, f = switching frequency = 2Gbps/pin, N = number of bits = 1024

Power Delivery Network (LDO)

Without requiring the creation of an intricate switching-mode power supply with power rail capacitors, transistors, and inductors, an LDO provides a voltage conversion solution in a compact integrated package. A CPU with 50-100W of power can easily use an LDO. For GPU I/O’s, this package can convert 5V from an external adapter or power source to 3.3V. Nevertheless, the package dissipates heat because of low power efficiency. The system design engineers must ensure that the LDO can manage junction temperature at the maximum rated ambient temperature.

For example, A 5V LDO drawing 5A from load dissipates heat =(5-3.3)V * 5 A = 8.5W.

During the highest ambient temperature, the system design engineers must keep an eye on the local hotspot temperatures around output inductors, diodes, capacitors, and FETs to make sure they do not surpass approximately 180 °C, the PCB delamination temperature.

High Speed Lanes

High-speed lanes are present in edge modules. These lanes typically connect two CPUs, provide faster solid-state drives access, such as M.2, and high-speed NVMe SSDs that are faster than conventional SATA drives. Businesses engaged in network observability and troubleshooting can quickly replay recorded data to examine peak traffic pattern times during specific times of the week, thanks to these high-speed drives, which enable faster download speeds. A high-speed lane consumes up to 3-4 W per lane [5]. For example, a Jetson AGX contains 2 x8 or 16 PCIe Gen4 lanes, and these alone can consume 48W under peak load. NVIDIA Jetson Nano, known to run basic AI workloads, has a smaller form factor than AGX [6].

Figure 2: Compact Jetson Nano with Ethernet and USB capability.

Environmental Impact

Non-renewable energy sources account for the majority of the world’s electricity production. Instead of active cooling, embedded AI modules use thermal management materials to distribute heat. Typically, they are not mounted in 1U or 2U chassis and often installed in multiple quantities at the edge. By adding more air conditioners or portable fans, the edge facility must expand its HVAC capacity, which results in a higher grid power consumption. This increased demand for energy, especially when fossil fuels are used to create electricity, results in greenhouse gas emissions.

On top of the integrated circuit, which is a thin layer composed of wick and fluid materials, the tiny modules have a vapor chamber. Compared to a heat pipe, this vapor chamber provides superior heat management by dissipating heat in both directions.

The amount of AI tokens processed each month is increasing, and edge facilities wish to train more data. The hardware infrastructure must therefore be updated further. Because it readily holds lead, mercury, and cadmium, this leads to an increase in the amount of soil-contaminated electronic waste that needs to be disposed of.

References:

1. What is Edge AI and What is Edge AI used for? https://www.seeedstudio.com/blog/2020/01/20/what-is-edge-ai-and-what-is-it-used-for/?srsltid=AfmBOop-l62e7WN1T0_JEqSAux2v_FUy_fdtogXBwAveZnObavLGMI1E

2. Why GPUs Dominate AI: Unleashing the Power of Parallel Processing, https://www.gigenet.com/blog/why-gpu-and-not-cpu-for-ai-parallel-processing/#:~:text=Graphics%20Processing%20Units%20present%20a,GPUs%20feature%3A

3. Understanding thermal resistance, https://learn.sparkfun.com/tutorials/understanding-thermal-resistance/all

4. JEDEC Standard JESD235D: High Bandwidth Memory (HBM2E)

5. What are the power consumption differences between PCIe 4.0 and PCIe 5.0 data center GPUs? https://massedcompute.com/faq-answers/?question=What%20are%20the%20power%20consumption%20differences%20between%20PCIe%204.0%20and%20PCIe%205.0%20data%20center%20GPUs?#:~:text=Understanding%20these%20differences%20is%20crucial,comes%20with%20higher%20power%20demands.

6. The Hardware Pushing AI to the Edge https://www.eetasia.com/the-hardware-pushing-ai-to-the-edge/