AI/ML Offload Engines

Data center with rows of network servers

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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SiTime MEMS Timing Benefits

Complete MEMS clock tree

Precision MEMS Super-TCXO

Network synchronizer

More robust in real-world conditions

4x better dF/dT for accurate clocking

Resistant to airflow and heat

Immunity to power supply noise

Thin profile, easy to use

No cover or shielding

≤1 mm thin to fit back of a card

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 Offload Engines Block Diagram

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.

MEMS Timing for AI/ML Offload Engines

Devices Key Features Key Values
Network Synchronizer
SiT95148  1 to 220 MHz
  • 4 inputs, 11 outputs
  • Up to 2 GHz clock output frequencies
  • 120 fs [1] integrated phase jitter
  • Programmable PLL loop bandwidth, 1 mHz to 4 KHz
  • Digital frequency control
  • -40°C to +85°C
  • 9.0 x 9.0 mm package
  • Multiple clock domains, multiple clock outputs enables complex clock architectures
  • 10x more resistant to vibration and board bending
SiT5501 [2]  1 to 60 MHz
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  • ±10 ppb stability
  • ±0.5 ppb/°C
  • 2x10-11 Allan deviation
  • -40°C to 105°C
  • 7.0 x 5.0 mm package
  • Ensures QoS requirements are met in telecom equipment in hostile environments

[1] 12 kHz to 20 MHz integration range; [2]  Contact SiTime for higher frequencies

MEMS Timing Outperforms Quartz

Better Stability

Better Frequency Slope

SiTime – Better Stability
SiTime – Better Frequency Slope


Better Vibration Resistance

Better Allan Deviation

SiTime – Better Vibration Resistance
SiTime – Better Allan Deviation
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