ATS fanless, straight-fin heat sinks maximize system airflow for passive cooling of CPUs in a wide range of devices. These fanless, straight-fin heat sinks maximize system airflow to reliably cool high-performance processors at a lower cost than using heat sinks with fans. When attached with the available backing plate, these rugged heat sinks are usable on a wide variety of CPUs in industrial and commercial applications. Works with Intel, AMD, Nvidia CPUs, GPUs.
Available worldwide through our distribution network.
ATS’ high quality, low cost, aluminum stamped heat sinks are ideal for cooling TO-220s and other low power packages (e.g. TO-3s, TO-5s, TO-218s, TO-126s, TO-127s and TO-202s). They feature anodized material with solderable tabs. Stamped heat sinks are attached using clips, nuts or thermal adhesive tape. The simple design and manufacturing of these heat sinks allows high volume manufacturing and reducing assembly costs.
Expanding the Internet of Things (IOT) into time-critical applications such as with autonomous vehicles, means finding ways to reduce data transfer latency. One such way, edge computing, places some computing as close to connected devices as possible. Edge computing pushes intelligence, processing power and communication capabilities from a network core to the network edge, and from an edge gateway or appliance directly into devices. The benefits include improved response times and better user experiences.
While cloud computing relies on data centers and communication bandwidth to process and analyze data, edge computing provides a means to lay some work off from centralized cloud computing by taking less compute intensive tasks to other components of the architecture, near where data is first collected. Edge computing works with IoT data collected from remote sensors, smartphones, tablets, and machines. This data must be analyzed and reported on in real time to be immediately actionable. 
In the above edge computing scheme, developed by Inovex,
the layers are described as follows:
Cloud: On this layer
compute power and storage are virtually limitless. But, latencies and the cost
of data transport to this layer can be very high. In an edge computing
application, the cloud can provide long-term storage and manage the immediate
Node: These nodes are
located before the last mile of the network, also known as downstream. Edge nodes
are devices capable of routing network traffic and usually possess high compute
power. The devices range from base stations, routers and switches to
small-scale data centers.
gateways are like edge nodes but are less powerful. They can speak most common
protocols and manage computations that do not require specialized hardware,
such as GPUs. Devices on this layer are often used to translate for devices on
lower layers. Or, they can provide a platform for lower-level devices such as mobile
phones, cars, and various sensing systems, including cameras and motion
layer is home to small devices with very limited resources. Examples include
single sensors and embedded systems. These devices are usually purpose-built
for a single type of computation and often limited in their communication
capabilities. Devices on this layer can include smart watches, traffic lights and
environmental sensors. 
Today, edge computing is becoming essential where time-to-result must be minimized, such as in smart cars. Bandwidth costs and latency make crunching data near its source more efficient, especially in complex systems like smart and autonomous vehicles that generate terabytes of telemetry data. 
Besides vehicles, edge computing examples serving the IoT include
smart factories and homes, smartphones, tablets, sensor-generated input,
robotics, automated machines on manufacturing floors, and distributed analytics
servers used for localized computing and analytics.
Major technologies served by edge computing include
wireless sensor networks, cooperative distributed peer-to-peer ad-hoc
networking and processing, also classifiable as local cloud/fog computing,
distributed data storage and retrieval, autonomic self-healing networks, remote
cloud services, augmented reality and virtual reality. 
Autonomous Vehicles and Smart Cars
New so-called autonomous vehicles have enough computing
hardware they could be considered mobile data centers. They
generate terabytes of data every day. A single vehicle running for 14 to 16 hours
a day creates 1-5TB of raw data an hour and can produce up to 50TB a day. 
A moving self-driving car, sending a live stream continuously to servers, could meet disaster while waiting for central cloud servers to process the data and respond back to it. Edge computing allows basic processing, like when to slow down or stop, to be done in the car itself. Edge computing eliminates the dangerous data latency.
Once an autonomous car is parked, nearby edge computing
systems can provide added data for future trips. Processing this close to the
source reduces the costs and delays associated with uploading to the cloud.
Here, the processing does not occur in the vehicle itself.
Other Edge Computing Applications
Edge computing enables industrial and healthcare providers
to bring visibility, control, and analytic insights to many parts of an
infrastructure and its operations—from factory shop floors to hospital
operating rooms, from offshore oil platforms to electricity production.
Machine learning (ML) benefits greatly from edge computing.
All the heavy-duty training of ML algorithms can be done on the cloud and the
trained model can be deployed on the edge for near real-time or true real-time
For manufacturing uses, edge computing devices can translate data from proprietary systems to the cloud. The capability of edge technology to perform analytics and optimization locally, provides faster responses for more dynamic applications, such as adjusting line speeds and product accumulation to balance the line. 
Edge Computing Hardware
Processing power at the edge needs to be matched to the
application and the available power to drive an edge system operation. If
machine vision, machine learning and other AI technologies are deployed,
significant processing power is necessary. If an application is more modest,
such as with digital signage, the processing power may be somewhat less.
Intel’s Xeon D-2100 processor is made to support edge computing. It is a lower power, system on chip version of a Xeon cloud/data server processor. The D-2100 has a thermal design point (TDP) of 60-110W. It can run the same instruction set as traditional Intel server chips, but takes that instruction set to the edge of the network. Typical edge applications for the Xeon D-2100 include multi-access edge computing (MEC), virtual reality/augmented reality, autonomous driving and wireless base stations. 
Thermal management of the D-2100 edge focused processor is largely determined by the overall mechanical package the edge application takes. For example, if the application is a traditional 1U server, with sufficient air flow into the package, a commercial off the shelf, copper or aluminum heat sink should provide sufficient cooling. 
An example of a more traditional package for edge computing
is the ATOS system shown in Figure 6. But, for less common packages, where
airflow may be less, more elaborate approaches may be needed. For example, heat
pipes may be needed to transport excess processor heat to another part of the
system for dissipation.
One design uses a vapor chamber integrated with a heat sink. Vapor chambers are effectively flat heat pipes with very high thermal conductance and are especially useful for heat spreading. In edge hardware applications where there is a small hot spot on a processor, a vapor chamber attached to a heat sink can be an effective solution to conduct the heat off the chip.
The Nvidia Jetson AGX Xavier is designed for edge computing applications such as logistics robots, factory systems, large industrial UAVs, and other autonomous machines that need high performance processing in an efficient package.
Nvidia has modularized the package, proving the needed supporting semiconductors and input/output ports. While it looks like if could generate a lot of heat, the module only produces 30W and has an embedded thermal transfer plate. However, any edge computing deployment of this module, where it is embedded into an application, can face excess heat issues. A lack of system air, solar loading, impact of heat from nearby devices can negatively impact a module in an edge computing application.
Nvidia considers this in their development kit for this
module. It has an integrated thermal management solution featuring a heat sink
and heat pipes. Heat is transferred from the module’s embedded thermal transfer
plate to the heat pipes then to the heat sink that is part of the solution.
For a given edge computing application, a thermal solution
might use heat pipes attached to a metal chassis to dissipate heat. Or it could
combine a heat sink with an integrated vapor chamber. Studies by Glover, et al
from Cisco have noted that for vapor chamber heat sinks, the thermal resistance
value varies from 0.19°C/W to 0.23°C/W for 30W of power. 
A prominent use case for edge computing is in the smart factory empowered by the Industrial Internet of things (IIoT). As discussed, cloud computing has drawbacks due to latency, reliability through the communication connections, time for data to travel to the cloud, get processed and return. Putting intelligence at the edge can solve many if not all these potential issues. The Texas Instruments (TI) Sitara family of processors was purpose built for these edge computing machine learning applications.
Smart factories apply machine learning in different ways.
One of these is training, where machine learning algorithms use computation
methods to learn information directly from a set of data. Another is
deployment. Once the algorithm learns, it applies that knowledge to finding
patterns or inferring results from other data sets. The results can be better
decisions about how a process in a factory is running. TI’s Sitara family can execute a trained
algorithm and make inferences from data sets at the network edge.
The TI Sitara AM57x devices were built to perform machine
learning in edge computing applications including industrial robots, computer
vision and optical inspection, predictive maintenance (PdM), sound
classification and recognition of sound patterns, and tracking, identifying,
and counting people and objects. [18,19]
This level of machine learning processing may seem like it would require sophisticated thermal management, but the level of thermal management required is really dictated by the use case. In development of its hardware, TI provides guidance with the implementation of a straight fin heat sink with thermal adhesive tape on its TMDSIDK574 AM574x Industrial Development Kit board.
While not likely an economical production product, it
provides a solid platform for the development of many of the edge computing
applications that are found in smart factories powered by IIoT. The straight
fin heat sink with thermal tape is a reasonable recommendation for this kind of
Most edge computing applications will not include a lab bench or controlled prototype environment. They might involve hardware for machine vision (an application of computer vision). An example of a core board that might be used for this kind of application is the Phytec phyCORE-AM57x. 
Machine vision being used in a harsh, extreme temperature industrial environment might require not just solid thermal management but physical protection as well. Such a use case could call for thermal management with a chassis. An example is the Arrow SAM Car chassis developed to both cool and protect electronics used for controlling a car.
Another packaging example from the SAM Car is the chassis shown below, which is used in a harsh IoT environment. This aluminum enclosure has cut outs and pockets connecting to the chips on the internal PCB. The chassis acts as the heat sink and provides significant protection in harsh industrial environments.
Edge computing cabinetry is small in scale (e.g. less than 10 racks), but powerful in information. It can be placed in nearly any environment and location to provide power, efficiency and reliability without the need for the support structure of a larger white space data center.
Still, racks used in edge cabinets can use high levels of processing power. The enclosure and/or certain components need a built-in, high-performance cooling system.
Hardware OEMs like Rittal build redundancy into edge systems. This lets other IT assets remain fully functional and operational, even if one device fails. Eliminating downtime of the line, preserving key data and rapid response all contribute to a healthier bottom line.
Although edge computing involves fewer racks, the data needs vital cooling protection. For edge computers located in remote locations, the availability of cooling resources may vary. Rittal provides both water and refrigerant-based options. Refrigerant cooling provides flexible installation, water based cooling brings the advantage of ambient air assist, for free cooling. 
LiquidCool’s technology collects server waste heat inside a
fluid system and transports it to an inexpensive remote outside heat
exchanger. Or, the waste heat can be re-purposed. In one IT closet-based edge
system, fluid-transported waste heat is used for heating an adjacent room. 
Green Revolution Cooling provides ICEtank turnkey data centers built inside ISO shipping containers for edge installations nearly anywhere. The ICEtank containers feature immersion cooling systems. Their ElectroSafe coolant protects against corrosion, and the system removes any need for chillers, CRACs (computer room ACs) and other powered cooling systems. 
A Summary Chart of Suggested Cooling for Edge Computing
The following chart summarizes air cooling options for Edge Computing applications:
The Leading Edge
The edge computing marketplace is currently
experiencing a period of unprecedented growth. Edge market revenues are
predicted to expand to $6.72 billion by 2022 as it supports a global
IoT market expected to top $724 billion by 2023. The accumulation of IoT data,
and the need to process it at local collection points, will continue to drive
the deployment of edge computing. [28,29]
As more businesses and industries shift from enterprise to
edge computing, they are bringing the IT network closer to speed up data communications.
There are several benefits, including reduced data latency, increased real-time
analysis, and resulting efficiencies in operations and data management. Much critical
data also stays local, reducing security risks.
By Norman Quesnel
Senior Member of Marketing Staff
Advanced Thermal Solutions, Inc.
Rapid advancements in fiber optic technology have increased transfer rates from 10GbE to 40/100GbE within data centers. With the emergence of 100GbE technologies, the creation of data center network architectures free from bandwidth constraints has been made possible. The major enabler of this performance increase is the QSFP optical transceiver.
QSFP is the Quad (4-channel) Small Form-Factor Pluggable optical transceiver standard. A QSFP transceiver interfaces a network device, e.g. switch, router, media converter, to a fiber optic or copper cable connection as part of a Fast Ethernet LAN.
The QSFP design became an industry standard via the Small Form Factor Committee in 2009. Since then, the format has steadily evolved to enable higher data rates. Today, the QSFP MSA (multi-source agreement) specification supports Ethernet, Fibre Channel (FC), InfiniBand and SONET/SDH standards with different data rate options.
Fig. 1. The Small QSFP Form Factor Allows More Connectors and Bandwidth than Other Fiber Optic Transceiver Formats. Note the Cooling Fins on Each Receiver Device. 
The small QSFP form factor has significantly increased the number of ports per package. The increased density of transceivers can lead to heat issues. The optical modules can get hot due to their use of lasers to transmit data. Even though the popular QSFP28 provides lower power dissipation than earlier transceivers – abut 3.5W, the QSFP28 factor has also allowed a significant increase in port density.
Newer microQSFPs can dissipate even more heat. microQSFP interconnects fit more ports (up to 72) on a standard line card, saving significant design space.
Fig 2. Air Gap Locations Shown in Thermal Specifications Feature on QSFP. Top: QSFP at the Inside Edge of a Cage, Bottom: QSFP Section Showing Typical Internal Layout. 
The performance and longevity of the transceiver lasers depend on the ambient temperature they operate in and the thermal characteristics of the packaging of these devices. The typical thermal management approach combines heat dissipating fins, e.g. heat sinks, and directed airflow.
Fig 3. Test set-up of different heat sink designs on QSFP28 connector cages. (Advanced Thermal Solutions, Inc.)
Recently, Advanced Thermal Solutions, Inc. (ATS) tested a variety of pin and fin-style heat sinks for their comparative cooling performance on a standard QSFP connector cage. For this setup, an even amount of heat was provided to each connector site via a heater block. Individual thermocouples measured the heat flux resulting with the different heat sink types.
A main goal of this test was how each of four heat sinks would perform while relying on airflow incoming from just one side. By the time it reached the fourth heat sink would the airflow provide enough conduction for adequate cooling? An image from this series of tests is below in Figure 4.
Fig. 4. Test Setup to measure cooling performance of individual heat sinks on a QSFP connector cage when airflow is from one side only. (Advanced Thermal Solutions, Inc.)
The tests results showed that the denser the heat sink pins or fins on the sink closest to the incoming air, the hotter the farthest away QSFP will be. Thus, the best solution used heat sinks whose pin/fin layouts were optimized to work in the actual airflow reaching them.
This meant more open layouts closer to the air source, allowing more air to reach denser pin/fin sinks farther from the air. The non-homogeneous heat sinks allowed for a low, uniform temperature across the QSFP for the most effective function of the QSFPs’ lasers.
Cooling solutions are different between QSFP28 designs and microQSFP installations. QSFP28 transceiver cooling is typically provided at multiple connector sites. microQSFP modules, e.g. from TE Connectivity, have an integrated heat sink in the individual optical module. Used with connection cages that are optimized for airflow, their heat is controlled in high density applications.
Fig. 5. Integrated Module Thermal Solution (Fins) on microQSFPs Provides Better Thermal Performance and Uses Less Energy for Air Cooling. 
Fig. 6. A Video Demo from TE Connectivity Shows 72 Ports of microQSFP Transceivers Units Running at 5W Each and All Kept Under 55°C Temperature Using 82 CFM Airflow. 
Finally, another factor affecting cooling performance is surface finish and flatness. Designers can reduce thermal spreading losses by keeping the heat sources close to the thermal interface area and by increasing the thermal conductivity of the case materials.
For QSFP, the size of the cage hole for heat sink contact given in the multi-source agreement (MSA) can be increased giving a reduction in the thermal interface resistance and therefore module temperature.
National Thermal Engineer Day is finally here! Make sure to follow Advanced Thermal Solutions, Inc. (ATS) on Twitter (@qats) and on Instagram (@advancedthermsolutions) to see photos and watch videos from the ATS celebration.
Join in the social media conversation about this national day of recognition by using the hashtag #ThermalEngineerDay. Make sure to share your photos and the ways that you have taken time to celebrate the accomplishments and contributions of thermal engineers.