The explosive growth in data analytics is driving an intensely growing need for compute performance. We will review our ARPA-E PINE ENLITENED experimental testbed and simulation results to motivate performance advantages of disaggregation through the use of flexible photonic interconnect networks for distributed deep learning applications.
As bandwidth requirements and integration of photonic components in computing systems increase, the optical micro-ring resonator are becoming an important building block for dense, high-bandwidth interconnects. Ring resonators are small in size and can operate at data rates up to 60Gb/s NRZ,1 making them well-suited for integrating many rings operating at different wavelengths into a single device. These devices are a promising solution for complex interconnected systems, such as chip-to-chip interconnects.2 However, using these rings can be challenging as they are sensitive to fabrication and temperature variations and need constant tuning to lock them to their assigned optical wavelengths.3 This tuning is commonly done by inserting embedded heaters in or above the ring. Existing techniques that tune the rings by the optical power coupled into the rings require extensive characterization and spectrum analysis, or out-of-band signalling, to account for rings drifting across optical channels and fabrication variations. In this work, we use four cascaded micro-rings implemented in a silicon photonic device operating in the C-band. Each ring taps a single wavelength from a bus waveguide. The remaining light in the bus waveguide is then fed into a photodiode, which is used to monitor the tuning of all rings. We show an algorithm that, based only on information about the design of the chip, tunes the rings to the exact desired optical channel. With this algorithm the use of more complex techniques to directly measure a ring's coupled wavelength can be avoided, reducing system complexity.
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