Ultrafast optical circuit switching for data centers using integrated soliton microcombs.

Due to the slowdown of Moore's law, it will become increasingly challenging to efficiently scale the network in current data centers utilizing electrical packet switches as data rates grow. Optical circuit switches (OCS) represent an appealing option to overcome this issue by eliminating the need for expensive and power-hungry transceivers and electrical switches in the core of the network. In particular, optical switches based on tunable lasers and arrayed waveguide grating routers are quite promising due to the use of a passive core, which increases fault tolerance and reduces management overhead. Such an OCS-network can offer high bandwidth, low network latency and an energy-efficient and scalable data center network. To support dynamic data center workloads efficiently, however, it is critical to switch between wavelengths at nanosecond (ns) timescales. Here we demonstrate ultrafast OCS based on a microcomb and semiconductor optical amplifiers (SOAs). Using a photonic integrated Si3N4 microcomb, sub-ns (<520 ps) switching along with the 25-Gbps non-return-to-zero (NRZ) and 50-Gbps four-level pulse amplitude modulation (PAM-4) burst mode data transmission is achieved. Further, we use a photonic integrated circuit comprising an Indium phosphide based SOA array and an arrayed waveguide grating to show sub-ns switching (<900 ps) along with 25-Gbps NRZ burst mode transmission providing a path towards a more scalable and energy-efficient wavelength-switched network for data centers in the post Moore's Law era.

Design of interpolation functions for subpixel-accuracy stereo-vision systems.

Traditionally, subpixel interpolation in stereo-vision systems was designed for the block-matching algorithm. During the evaluation of different interpolation strategies, a strong correlation was observed between the type of the stereo algorithm and the subpixel accuracy of the different solutions. Subpixel interpolation should be adapted to each stereo algorithm to achieve maximum accuracy. In consequence, it is more important to propose methodologies for interpolation function generation than specific function shapes. We propose two such methodologies based on data generated by the stereo algorithms. The first proposal uses a histogram to model the environment and applies histogram equalization to an existing solution adapting it to the data. The second proposal employs synthetic images of a known environment and applies function fitting to the resulted data. The resulting function matches the algorithm and the data as best as possible. An extensive evaluation set is used to validate the findings. Both real and synthetic test cases were employed in different scenarios. The test results are consistent and show significant improvements compared with traditional solutions.