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Edge Computing’s Role in Digital Transformation

In this blog we talk about latest benchmarking results of our Scalable Edge Platform optimized for video analytics.

Michal Ptacek / March 07, 2023
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5G/6G usecases, digital transformation, COVID-19 pandemy - all of this is causing an explosion of data usage. Bringing computation closer to the location to resolve latency issues and solving problems with big data is needed.

According to several studies (*1) edge computing will grow at a compound annual growth rate (CAGR) of 21.6% between 2022 and 2028 to hit an estimated $156 billions by 2030. There are multiple factors behind that growth, one of them is the advancing of 5G/6G, which is enabling the establishment of new telco AI & IoT usecases. IoT is perceived as a key enabler for digital transformation and it's adding improvements in enterprise efficiencies. Tietoevry´s mission is to help our customers and partners with such digital transformation.

In Barcelona MWC2023 we demonstrated our edge computing usecase, which is showing Tietoevry capabilities in cloud edge computing domain. Together with our partners from Intel and Advantech we brought our Scalable Edge computing platform usecase (*2) into 2023 using newer version of all involved HW & SW components. Our usecase is demonstrating containerized Automatic Pedestrian Alert System (APAS) application, but in general any other edge usecase can be considered for such scalable edge platform solution. APAS application is designed to alert drivers about pedestrians potentially crossing the roads, which is especially crucial in difficult driving conditions or multi-lane support. This usecase was successfully presented in City of Tampere in 2019. (*3)

APAS application was onboarded on Intel Smart Edge Open (formerly known as OpenNESS), as it exposes Intel hardware features to the Kubernetes based containerized Edge environment and enables easy deployment and optimized orchestration of various usecases starting from media analytics through Content Delivery Network (CDN) up to 5G access and core network functions.

HW Platform used in this case is based on Advantech SKY-8132S (*4), which is a compact 1U edge server based on 3rd Gen Intel Xeon Scalable processors. For the scaling scenario we are using also Advantech VEGA-3500 (*5), which is Intel 11th Gen CPU-based UHD video accelerator card.

Similar benchmarking was done during 2021 on top of following HW & SW stack.


We have used the same key performance indicators as in 2021. Our goal is to demonstrate how many different camera streams can be processed on single low/mid cost edge platform and how the capabilities of such edge platforms are evolving over time. We have also used the same 3fps input camera streams and Intel OpenVINO pretrained object detection (*6) with SSD (Single Shot MultiBox Detector) deep learning model and FP16 precision. Focus areas are zebra crossings and crossroads are reframed to 512x512 pixels for inference processing. The idea is to keep the test environment as close to the original benchmarking, which was done in 2021 but use newer HW & SW stack to visualize the progress.

Based on that criteria we have performed a range of tests and focused on 3 main scenarios:

  1. to indicate how many parallel camera streams we can run on SKY-8132S and compare it with our previous results from SKY-8101.
  2. to indicate how many parallel camera streams we can run on VEGA-3500, which is PCIe UHD card and compare it it older but VPU specialized VEGA-340 used during 2021 benchmarking.
  3. to indicate how many parallel camera streams we can run on whole platform using combined computing power of SKY-8132S CPU and VEGA-3500 card and compare this with previously tested Scalable edge platform from 2021 benchmarking.


We have collected following data, which is very good and in some extent better than what we expected. Average inference is on acceptable level and it indicates that so many camera streams can be processed. However there were some peaks detected, where inference took much longer than what is acceptable. It occured on VEGA-3500, where we hit also memory limitation (VEGA-3500 has own RAM memory 2x 16GB) and there was some disc swapping happening. We believe that this can be further optimized in APAS code for instance by having a dedicated container for OpenVINO processing instead of having multiple dedicated container for each camera stream. Our team is already now working on how to optimize this further.


Minimal inference

Average inference

Maximal inference

Load Level



1.85 ms

106.270 ms


50 streams

Host CPU only


77 ms

40.967 ms


15+15 streams

VEGA-3500 only


78 ms

73.621 ms



Both host CPU and VEGA-3500


We successfully demonstrated significant improvements when compared 2nd Gen Intel Xeon CPU with it’s successor from 3rd Gen Intel Xeon CPU family. Newer VEGA-3500 card is also bringing significant boost to scaling capabilities of such edge platform. VEGA-3500 provides a independent computing unit with dedicated RAM, CPU and OS. So benchmarking executed on VEGA-3500 did not impact results on underlying host tests, which was not the case during 2021 tests with VEGA-340. VEGA-3500 provides even better scaling options as several such cards can be inserted into SKY-8000 Advantech platforms.

Edge computing is an important area for Tietoevry, so further improvements on this usecase is planned, as well as other exciting usecases and research work. So more updates will follow, so stay tuned.



Michal Ptacek
Software Engineering Manager


Michal Ptacek

Software Engineering Manager

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