PsiQuantum, a company known for its quantum computing work, announced the development of an optical switch designed for AI data centers. This switch utilizes low-loss silicon photonics, a technology derived from their quantum computing research. The prototype is an 8x8 configuration, meaning it has eight inputs and eight outputs.
The new optical switch demonstrated a typical signal loss of 1.2 dB in lab tests, which is significantly lower than the 3 dB loss typically tolerated by data center optics. This low loss is achieved without the need for an amplifier. Unlike traditional data center switches that convert optical signals to electrical signals at each hop, PsiQuantum's optical circuit switch (OCS) passes light directly through, maintaining a fixed light path until a change is ordered. This all-optical operation reduces power consumption and latency.
The development of this optical switch is relevant as AI clusters continue to grow, where moving data between GPUs is becoming a significant bottleneck. Improving communication bandwidth and distance between GPUs is crucial for enabling different racks of GPUs to operate cohesively. The switch's ability to maintain signal integrity over longer distances and without electrical conversion helps address these challenges.
While the PsiQuantum prototype is currently an 8x8 device, smaller than competitors like Google's Palomar (136 ports) or Lumentum's (300 ports), the company states that larger devices are in development. Other optical switches from companies like Salience Labs and iPronics also achieve low signal loss (under 2 dB) but often incorporate built-in amplification. PsiQuantum's switch uses circuits etched on a silicon chip with no moving parts, differing from mirror-based microelectromechanical systems (MEMS) used by some competitors.
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PsiQuantum, a quantum computing company, has developed an 8x8 optical switch using low-loss silicon photonics for AI data centers. This switch achieves a typical signal loss of 1.2 dB, which is lower than many existing optical switches, and operates without converting optical signals to electrical signals. The technology aims to address data transfer bottlenecks in growing AI clusters by improving communication bandwidth between GPUs.