Welcome to the second article from Supermicro on Retail Loss Prevention. The first article described the challenges that retailers face when dealing with inventory loss and the characteristics of a retail loss prevention solution. This article will take a deeper dive into the software and hardware technology that will power a retail loss prevention solution.

AI Building Blocks

To make it easier for developers to quickly build and roll out AI-based applications designed to prevent retail theft, NVIDIA has created a library of AI workloads. For retail, this includes the Retail Loss Prevention AI Workflow, the Multi-Camera Tracking AI Workflow, and the Retail Store Analytics Workflow, built on its Metropolis microservices. These workflows can be used as no-code or low-code building blocks for loss prevention applications because they come pretrained with images of the most-stolen products (such as meat or alcohol). Additionally, they come with software to plug into existing store applications for point-of-sale machines as well as object and product tracking across entire stores.

The workflows mentioned above feature an advanced variation of few-shot learning, which means that an AI model only needs to process a relatively small set of images to get going. It is designed to adapt continuously with limited new product data, using object characterization and self-supervised learning algorithms. This unique method of active learning identifies and captures new products and packaging changes scanned during checkout for future recognition.

NVIDIA’s AI workflows can be accessed through cloud-native microservices with NVIDIA AI Enterprise software. This enables developers to jump-start development by easily customizing the workflow to their requirements. Next, they can rapidly index hundreds of thousands of store products for cross-camera and barcode-scan identification, and start deploying their application at scale. For retail organizations, the availability of these workflows means that it becomes much more economically feasible to develop and adopt AI-based solutions, while improving the quality and speed of the outcome.

At the core of any AI solution is the hardware that it runs on, specifically NVIDIA accelerated computing including Graphical Processing Units (GPUs). GPUs are accelerator cards that are the true workhorses of AI. GPUs ingest data from camera feeds and run AI models governed by the Retail AI Workflows to look for anomalies.

Right-sized Infrastructure

It can take several cameras to cover Self-checkout (SCO) systems properly, which means there are multiple video streams to analyze. These streams must be processed simultaneously, in real-time without lag or interruptions. With that in mind, for the best results, inferencing should occur locally at the retail location instead of sending the video over the internet to a cloud-based server. This is why specialized Edge AI systems, such as those from Supermicro, are ideal for these workflows.

With the most comprehensive product line of edge AI servers on the market, Supermicro is perfectly suited to round-out the loss prevention solution. For optimal results and ROI, it is important that systems are “right-sized” to meet the demands of the specific deployment, while keeping TCO within budget. With a broad range of form factor sizes and configurations, Supermicro can accommodate the most appropriate NVIDIA GPU for the job.

Unlike data center systems, edge servers are meant to be deployed in (mostly) unconditioned environments. Edge systems can be mounted directly to a wall, placed on a shelf, or installed in two-post racks that are commonly used for network equipment. Combined with wider operating temperatures, these systems provide great deployment versatility.

Various deployment options and the ability to operate in typical store conditions are great features, but they only speak to part of the necessary infrastructure solution. Systems also need to be capable of performing critical AI inferencing tasks. Optimized for maximum AI power, Supermicro has the edge AI portfolio built to operate the latest NVIDIA accelerated computing and GPUs of all sizes. From low-profile cards such as the NVIDIA L4 all the way up to the large double-width accelerators like the NVIDIA L40S GPU. The flexibility in system configuration enables organizations to match the AI server to the required performance exactly.

Target Systems

For deployments where there is limited space and wall-mounting is the preferred method of installation, Supermicro has multiple solutions. The SYS-E403 Box PC and the SYS-E300 Mini-1U offer great performance in a small package. Sites with small networking racks can take advantage of the short-depth rackmount products, such as the SYS-E112B Network Edge server or the low-power SYS-510D.

Locations with kitchens, especially those with fryers, can generate particulates that are detrimental to electronics. This can lead to system failures or shorter lifespans. Supermicro can address these environments with our fan-less products such as the compact SYS-E302 edge server.

On the higher end of the spectrum, large format stores with several SCOs may require more than one NVIDIA GPU to handle the streams from dozens of cameras. This is where high-density, 2U Hyper-E family of products come into play.

Conclusion

What does all this mean for you, the retailer? By combining the advanced AI software solutions from NVIDIA and the best-of-breed enterprise edge servers from Supermicro with NVIDIA accelerated computing, retailers finally have access to solutions that can address the ever-growing shrinkage problem. Supermicro’s software and services partners can leverage these elements to create efficient AI solutions that help ensure low TCO and fast ROI.

If you would like to learn more about our combined AI solutions for retail, please reach out or visit the website: https://www.supermicro.com/en/solutions/ai/retail

For more information about the NVIDIA Loss Prevention Workflow, check out the technical documentation: https://docs.nvidia.com/mms/text/Smart_self_checkout_Overview.html

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