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The two CNNs were run separately and on individual processing elements, even within the same device. The ComBox evaluation used image input sizes of 768 x 512 and 576 x 384 pixels for the U-Net algorithm, and 256 x 256-pixel image data for DarkNet-19. They tested these platforms using two popular image classification convolutional neural networks (CNNs): U-Net and DarkNet-19. These include chips, graphics cards, and accelerator modules from multiple vendors The ComBox engineering team began their cost-per-FPS experiment with several options for the main deep-learning processor. “What we found is that the Intel ® NUC8i5BEK, based on 8 th generation Intel ® Core ™ processors, provided the most value in these workloads-with an average cost per FPS of just over $4.00 per month,” says Dmitriy Rytvinskiy, general director at ComBox.
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They measured different options based on cost per frames per second (FPS) of executing AI algorithms-a key measurement for calculating ROI in computer vision systems. They want to know about features and optimizations that can boost efficiency, reduce cost, lower power consumption, and enable new capabilities.īut evaluating multiple solutions against these parameters can take a lot of time.Įngineers at ComBox Technology, an IT and neural networks systems integrator, made time to benchmark several computing solutions, before designing its AI server. What matters to design engineers is how processors fare in particular use cases. These include well-known processing options like CPUs and GPUs, and more novel solutions like vision processing units (VPUs).īut as these devices make their way to deployment in real-world systems, the datasheet performance specifications become essentially meaningless. As a result, a slew of computing products have been introduced or reinvented to serve AI use cases. Processor manufacturers are racing to entrench themselves in the growing AI market.