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What Is Lab Automation?

Lab automation uses sophisticated laboratory information from management systems (LIMSs) and automated laboratory instruments to deliver precise, accurate test results at scale. These smart laboratory instruments use onboard processors to drive robotics, support Internet of Things (IoT) technologies, and perform sophisticated analysis.

Intel and Intel partners work closely with laboratory instrument manufacturers to help optimize applications, introduce innovative new technologies, and meet changing customer needs.

Build for Today, Design for Tomorrow

The instruments you ship this quarter will be in service for years to come. For instruments to stay relevant, they must be able to adapt to rapid innovation especially in the areas of analytics and AI.

Laboratory instruments, powered by Intel®-based hardware, development tools and software, can access the computing performance they need to process intense workloads, adapt to next-level automation, and securely manage unprecedented amounts of data.

The Future of Lab Automation

Intel is helping manufacturers integrate new technologies that deliver value today and set the foundation for the future.

Frequently Asked Questions

Lab automation uses sophisticated laboratory information management systems (LIMSs), robotic material handlers, and increasingly smart laboratory instruments to track test samples, evaluate results, and perform labor-intensive clinical tasks.

Lab automation is used in clinical and biopharmaceutical research labs as well as manufacturing facilities. Materials research labs use automation to discover new compounds and molecules.

Basic lab automation depends on automated laboratory instruments with onboard computing and high-speed networking. Instruments are assembled to create workflows that are managed by automated control systems and information management systems. High-performance workstations and on-premise or cloud-based servers support lab-wide automation and operational technology.

Artificial intelligence is making lab automation smarter and more autonomous. AI requires cameras, microphones, and other sensors to capture data plus additional software and computing power.

1. KFBIO Cervical Cancer Screening OpenVINO™ Model Throughput Performance on Intel® Xeon® Gold 6148 Processor:

NEW:

Test 1: Tested by Intel as of 6/15/2019. Two-socket Intel® Xeon® Gold 6148 processor, 20 cores, HT on, turbo on, total memory 192 GB (12 slots/16 GB/2,666 MHz); BIOS: SE5C620.86B.0X.01.0007.062120172125 (ucode: 0x200004d), CentOS Linux release 7.5.1804 (Core); Deep Learning Framework: Keras 2.2.4 and Intel-optimized TensorFlow: 1.13.1; topology: RetinaNet: https://github.com/fizyr/keras-retinanet; compiler: gcc 4.8.5, MKL DNN; version: v0.17, BS=8, both synthetic data and customer data, one instance/two socket, datatype: FP32.

Test 2: Tested by Intel as of 6/15/2019. Two-socket Intel Xeon Gold 6148 processor, 20 cores, HT on, turbo on, total memory 192 GB (12 slots/16 GB/2,666 MHz); BIOS: SE5C620.86B.0X.01.0007.062120172125 (ucode: 0x200004d), CentOS Linux release 7.5.1804 (Core); Intel® software: OpenVINO™ R2019.1.1094; topology: RetinaNet: https://github.com/fizyr/keras-retinanet; compiler: gcc 4.8.5, MKL DNN; version: v0.17, BS=1, eight asynchronous requests, both synthetic data and customer data, one instance/two socket; datatype: FP32.

BASELINE:

Tested by Intel as of 6/15/2019. Two-socket Intel Xeon Gold 6148 processor, 20 cores, HT on, turbo on, total memory 192 GB (12 slots/16 GB/2,666 MHz); BIOS: SE5C620.86B.0X.01.0007.062120172125 (ucode: 0x200004d), CentOS Linux release 7.5.1804 (Core); Deep Learning Framework: Keras 2.2.4 and Vanilla TensorFlow: 1.5; topology: RetinaNet: https://github.com/fizyr/keras-retinanet; compiler: gcc 4.8.5, MKL DNN version: v0.17, BS=8, both synthetic data and customer data, one instance/two socket; datatype: FP32.