AI IN RETAIL

AI IN THE RETAIL MARKET IS ESTIMATED TO BE WORTH OVER $17 BILLION BY 2028 AND IS THE SECTOR MOST READILY ADOPTING AI-BASED TECHNOLOGIES.^[1]

Already, AI is used to forecast market conditions and analyze business trends. With the growth of monitoring technology, AI solutions can also provide insight within physical stores, allowing businesses to track store occupancy and monitor queues. Advancements in AI are so promising that checkout-free technologies can analyze individual items within smart stores, potentially eliminating traditional registers. AI is also used in automatic inventory management to reduce shrinkage in stores.

Habana offers hardware and software solutions that increase the pace of research by shortening the time of AI experiment cycles. Habana’s AI solutions reduce development and validation costs, letting researchers explore more within their budget.

Applications of AI in retail

With the help of AI, retail stores can transform existing surveillance systems into powerful analytical tools that can optimize operations and improve customer experience.

AI computer vision models can be used to identify people and their demographic data, allowing businesses to know the number of customers shopping in store in real-time, as well as basic data like gender, age group, and how much time spent in specific areas of the store.

Similarly, AI solutions can improve inventory management. Using predictive analysis, business owners can forecast demand to ensure they have the right products available in the right store at the right time. Some retailers have built AI robots that manage inventory, detecting misplaced, mispriced, and out-of-stock items. AI technology arms retailers with more accurate inventory management, reducing shrinkage by providing tools to monitor stores and warehouses actively and automatically.

Checkout-free stores are the most advanced AI application in retail. When an item is selected, various sensors process which item was selected by which customer in real-time and charges the customer when they leave the store, eliminating the need for a checkout register. CV-based solutions for smart stores need iterative targeted model retraining as new SKUs are frequently added to the store inventory. NLP models are also useful for tracking customer feedback, categorizing products in e-commerce sites, and detecting fraud.

Challenges

According to the Food Marketing Institute, a traditional supermarket has anywhere from 15,000 to 60,000 SKUs. Checkout-free technology is a vision-based system, so the object detection model will need to recognize all these items. On top of that, new products come to market every day. These models require frequent retraining in order to generalize to more stores and cover more items. Large input images are favorable to reach the desired detection accuracy and will require heavier computing and increased memory.

Why is gaudi a good fit for retail Use Cases?

Training deep object detection and tracking models for checkout-free stores require a large amount of processing that can be paralleled and thus accelerated. They can benefit specifically from accelerators that can handle data parallelism when the training dataset is huge or distributed and model parallelism when the models are large.

The two primary considerations that come into play when employing AI processing are time- and cost-to-train. Habana’s Gaudi Training Processors are expressly designed—in both hardware and software—to deliver high-efficiency cost- and time-to-train, making AI training more accessible to more organizations and for more applications.

First-generation Gaudi delivers up to 40% better price-performance than comparable GPU-based solutions—for both the EC2 DL2 instance and on-premise systems. Training with Gaudi clusters is available both in the cloud with AWS EC2 DL1 instances consisting of 8 Gaudis and on-premises with the Supermicro X12 Gaudi Training Server also consisting of 8 Gaudis. In addition, Gaudi2, which launched in May 2022, offers substantial performance advances that enable significantly faster training of models while preserving cost-efficiency. Gaudi2 systems will be available from Supermicro in 2H 2022 for on-premises implementation.

News and customer testimonials

“Our experiences with Gaudi give us confidence that we will be able to lower our training costs while improving model quality and translate that into even more powerful tools for our end-user.”

Maxime Bergeron
R&D Director
Riskfuel

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[1] Vantage Market Research, “Artificial Intelligence (AI) in Retail Market Size to Reach 17,086.54 USD Million by 2028 | Increasing AI-powered Application and Chatbots in the Retail Industry Flourish the Market”, March 2022.