Overview

In the life sciences industry, the demand for automation and precision in biological sample imaging is transforming how research laboratories capture, analyze, and classify data. A U.S.-based research automation company sought to advance its imaging systems using AI to detect sample conditions directly at the edge. To achieve this, the company turned to Premio’s BCO-6000-RPL Series High-Performance Industrial Computer, combining Intel’s latest Core i9 performance with GPU acceleration for high-speed, reliable AI inference and model training at the edge.

Challenges

Solution

Benefits

Company Overview

The company specializes in laboratory automation platforms that combine robotics, imaging, and AI to streamline biological research workflows. Its innovative solutions enable faster, more consistent experimentation and data collection. By integrating edge computing and wireless connectivity, the company continues to push the boundaries of intelligent laboratory automation across the U.S. and global life sciences markets. 

The Challenges

Overcoming CPU Bottlenecks for AI Inference 

The company’s existing systems struggled to meet the processing demands of PyTorch-based models. The inability to fully utilize multi-threaded CPU power led to delays in training and inference, limiting real-time image classification performance in critical experiments. 

Handling High-Speed Data from Multiple Cameras

Each IP camera continuously captured and streamed high-resolution biological sample images to the edge computer. Conventional storage systems couldn’t handle the volume and speed required for AI training datasets, causing potential bottlenecks during simultaneous data writes and reads. 

Expanding GPU Resources for Machine Learning

High-performance GPU integration was crucial for training and inference acceleration. However, the lab’s compact setups required a space-efficient solution capable of housing a discrete GPU without compromising reliability or thermals. 

Managing Connectivity for Multi-Camera Systems

The imaging environment demanded both wired and wireless options to connect IP cameras and auxiliary devices across different lab stations. The absence of built-in Wi-Fi limited deployment flexibility and required additional external adapters, increasing setup complexity.

Ensuring Reliability and Certification Compliance

Continuous laboratory operation required hardware that could run 24/7 under strict performance and safety standards. The system needed UL-certified reliability to meet industrial safety requirements and ensure long-term dependability in sensitive environments.

The Solution

High-Performance Edge Computing with Premio’s BCO-6000-RPL

Premio’s BCO-6000-RPL provided exceptional processing power through its Intel Core i9-13900TE CPU, delivering the multi-core performance needed for concurrent AI workloads at the edge. The rugged, compact design ensured long-term operational reliability under continuous use.

High-Speed NVMe Storage for Continuous Image Data

The built-in M.2 NVMe slot supported ultra-fast SSD storage, allowing rapid write and access speeds for high-resolution camera feeds. This ensured uninterrupted image ingestion and efficient data handling during AI model training and inference.

GPU Expansion for Deep Learning Acceleration

With its PCIe x16 slot, the BCO-6000-RPL seamlessly supported an NVIDIA RTX 4000 SFF Ada GPU, dramatically enhancing the system’s capacity for PyTorch-based AI workloads. This enabled real-time classification and analysis directly within the laboratory environment.

Built-In Wi-Fi 6 and Bluetooth 5 for Flexible Deployment

Through its M.2 E-Key slot, the system supported Intel Wi-Fi 6 and Bluetooth 5 wireless modules, allowing flexible and clutter-free installations across lab environments. This enabled faster setup, remote access, and reliable data transmission between stations without extensive cabling.

Industrial Reliability and UL Certification

Built with industrial-grade components and validated under UL 62368-1, CE, and FCC Class A standards, the BCO-6000-RPL delivered mission-critical reliability and safety compliance. Premio’s Los Angeles-based engineering team provided responsive local support and long-term lifecycle assurance.

The Benefits

AI-Ready Wireless Edge Performance

The combined power of Intel’s latest CPU, GPU acceleration, and Wi-Fi 6 connectivity allowed real-time training and inference to occur directly at the edge, minimizing latency and enabling rapid model iteration.

Flexible Integration and Scalability

With rich I/O and wireless capabilities, the system seamlessly supported multiple IP cameras, enabling scalable laboratory setups across different imaging stations without additional infrastructure.

Continuous, Reliable Operation with Local Support

Premio’s UL-certified design and accessible support from its Los Angeles facility ensured the system’s longevity and uninterrupted performance in critical research environments.

Conclusion

By integrating Premio’s BCO-6000-RPL Series High-Performance Industrial Computer, the life sciences automation provider successfully merged AI computing, GPU acceleration, and wireless connectivity into a compact edge platform. The result was a faster, more flexible imaging workflow capable of performing real-time AI inference and continuous learning directly within the lab. This collaboration exemplifies how industrial-grade edge systems are advancing intelligent automation in biological research.