Offload engines are increasingly being deployed to speed AI/ML applications in datacenter environments that can be hot and dense. SiTime MEMS precision timing solutions deliver the performance and stability over temperature required to clock AI/ML workloads. Our network synchronizers provide multiple clock outputs for complex architectures. Our Super-TCXOs provide excellent clock stability under fast temperature ramp and airflow.

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AI/ML (artificial intelligence / machine learning) workloads in datacenter applications are increasingly being offloaded to flexible FPGA-based subsystems. These FPGA-based AI/ML offload engines are more power and compute efficient compared to GPU-based systems.

Offload engines are special-purpose hardware platforms for very specific computational needs. In datacenters, offload engines are increasingly being deployed to speed AI/ML applications. Cloud computing has naturally enabled aggregation of large datasets. The adoption of AI and ML techniques to speed analyses of data or to look of novel applications of existing data continues to accelerate.

SiTime network synchronizer products, along with precision TCXOs and OCXOs, are key technology enablers for precise timekeeping in datacenters that deploy AI/ML offload engines.

AI/ML workloads can be efficiently handled with specialized hardware such as compute platforms based on graphics processing chips from Nvidia (as an example). Another trend in datacenters is the adoption of distributed computing. Large workloads are distributed across HW racks that have multiple general-purpose CPUs (from Intel or ARM) and local memory. Precise timekeeping is therefore critical in scheduling the workloads and maintaining correctness and coherency of the datasets. AI/ML offload engines are expensive resources and ensuring high utilization of these offload engines is a key system design goal.

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