Accelerate innovation in Retail AI

Computer vision AI-enabled Point of Sales

Computer vision AI significantly improves retail operations, such as preventing label switching at self-checkout points. This technology uses object detection networks to ensure that scanned items match their labels, reducing errors and enhancing user experience. Using video recognition instead of weight and scale measurements enables self-checkouts to become more efficient and user-friendly.

However, the high costs associated with current hardware solutions, which often rely on expensive GPUs, hinder large-scale implementation. This financial barrier often results in innovations getting stuck in the pilot phase, preventing widespread adoption.

Axelera AI addresses these challenges by integrating the computing power of a cloud data center into standard workstations or industrial PCs. This approach allows OEMs and solution providers to use standard camera infrastructures combined with next-gen Edge AI technology at a fraction of the cost. This setup not only accelerates innovation in retail AI but also reduces energy costs by tenfold, enabling deployment in uncontrolled environments while maintaining reliability and significantly lowering the total cost of ownership.

Optimize shopper behavior and curbside pickup

Computer vision AI significantly enhances retail operations by monitoring shopper traffic and queuing in supermarkets and malls. This technology optimizes staff shifts to reduce wait times and improve the shopping experience. By analyzing video data, AI can provide real-time insights into customer flow, helping retailers make data-driven decisions about staffing and layout adjustments.

Additionally, computer vision can recognize car license plates, streamlining curbside pickup processes and improving reliability. This ensures that orders are accurately matched with arriving vehicles, reducing errors and enhancing customer satisfaction.

These advancements showcase how computer vision AI-driven solutions can transform retail efficiency and customer service.

Enabling large scale autonomous stores

In retail environments, combining object detection, classification, and pose estimation is crucial for tasks like automated inventory management, customer behavior analysis, and enhancing shopping experiences. For example, a system can use object detection to identify products, classification to verify items, and pose estimation to understand customer interactions with products on shelves. The Axelera AI Metis design, with its four self-sufficient cores, efficiently handles these tasks in parallel, significantly enhancing performance.

By utilizing existing camera infrastructures, Axelera AI's solution avoids the substantial investment required to replace cameras with expensive AI-equipped alternatives. This makes it feasible to scale such solutions across large stores. Moreover, Axelera AI’s hardware brings data center-level performance into a compact and cost-effective form factor, reducing the total cost of ownership and enabling widespread deployment in retail settings.