W H I T E PA P E R

Rethink What's Possible:
Pushing Computer Vision
Boundaries Beyond 4k

Computer vision and image processing algorithms areincreasingly common to commercial and research-relatedapplications. These high resolution models perform complex
computations to process image data in real-time and require

What’s Inside
Surpass The Limitsof The GPU

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Adding More GPUs Isn’t
The Answer

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Train Large Computer
Vision Models With
High-Resolution Images

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Rethink What’s Possible

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rapid processing to yield analysis and results.
In the context of machine learning image processing andanalysis, resolution is everything. An image’s resolution canenable a more detailed, meaningful analysis that results ingreater understanding. To this end, a high-resolution image
will contain more information and detail than a low-resolution
image of the very same subject.
Today, higher-resolution image processing requires significant
computational capabilities. So much so, in fact, that training
models to use these high-resolution images have renderedcurrent state-of-the-art technologies unusable.
When it comes to high-resolution processing, legacy
architecture constraints are holding back researchand technology advances across numerous use cases,including in areas such as autonomous driving, oil andgas exploration, medical imaging, anti-viral research,astronomy, and more.

Rethink What’s Possible: Pushing Computer Vision Boundaries Beyond 4K

Surpass The Limits of The GPU
SambaNova Systems has been working with industry partners
to develop an optimized solution for training computer
vision models with increasingly growing levels of resolution— without compromising high accuracy levels. We take a“clean sheet” complete systems approach to enable nativesupport for high-resolution images. Co-designing across
our complete stack of software and hardware provides
the freedom and flexibility from legacy GPU architecture
constraints and legacy spatial partitioning methods.

Adding More GPUs Isn’t The Answer
If you consider images in the context of AI/ML training data,
the richer and more expansive your training information (i.e.,images), the more accurate your results can be.
Using a single GPU to train high-resolution computer visionmodels predictably results in “Out of Memory” errors. On theother hand, clustering multiple GPUs brings all the challenges
of disaggregation of the computational workflows onto eachindividual GPU in the cluster to aggregate GPU memory.
In this case, this is not merely clustering a few GPUs in a single
system, but aggregating hundreds, if not thousands of GPUdevices. In addition, conventional data parallel techniquesthat slice the input image into independent tiles deliver lessaccurate results than training on the original image.

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Rethink What’s Possible: Pushing Computer Vision Boundaries Beyond 4K

SambaNova DataScale o ers anew way to run computer vision
workloads out of the box

Train Large Computer Vision Models With High-Resolution Images
Massive Data: A single SambaNova DataScale ® system — with petaflops of performance andterabytes of memory — is built with a dataflow architecture design. This co-design of software andhardware properties is built to enable high performance processing of a range of complex structures
such as high-resolution images, pushing computer vision boundaries far beyond 4k.
SambaFlow™ Software: The SambaFlow software stack transforms deep learning operations to work
seamlessly. SambaFlow native software support for tiled input images, intermediate tensors, andconvolution overlap handling are all automated. The results are equivalent to the non-tiled version
and require no changes to the application or programming model.
Robust Architecture: SambaNova’s Reconfigurable Dataflow Architecture™ is critical for efficient
processing of input image tiles and is fully materialized in device memory, unlike with non-Dataflow
devices, such as GPUs.

Rethink What’s Possible
The resulting solution is capable of processing images of extremely high resolution — from 4k up
to 50k — on a single DataScale system. End users then have the option of scaling up additional
DataScale compute resources to further reduce training times while maintaining high levels ofutilization and accuracy.
The high-resolution image processing breakthroughs achieved on SambaNova DataScale allow
organizations to cut years of development time, significantly simplify architecture, and easeprogrammability. All this while yielding state-of-the-art results and capabilities.
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Learn more at SambaNova.ai
linkedin.com/company/sambanova@SambaNovaAI@SambaNovaAI

info@sambanova.ai

AI is here. With SambaNova, customers are deploying the power of AI and deep learning in weeks rather than years to meet the demandsof the AI-enabled world. SambaNova’s flagship offering, Dataflow-as-a-ServiceTM, is a complete solution purpose-built for AI and deeplearning that overcomes the limitations of legacy technology to power the large and complex models that enable customers to discover
new opportunities, unlock new revenue and boost operational efficiency. Headquartered in Palo Alto, California, SambaNova Systems
was founded in 2017 by industry luminaries, and hardware and software design experts from Sun/Oracle and Stanford University. Investors
include SoftBank Vision Fund 2, funds and accounts managed by BlackRock, Intel Capital, GV, Walden International, Temasek, GIC,Redline Capital, Atlantic Bridge Ventures, Celesta, and several others. For more information, please visit us at sambanova.ai or contact us
at info@sambanova.ai. Follow SambaNova Systems on LinkedIn.

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