A comparison of a digital electronic engine, and our photonics in-memory computing engine, for processing data streams for correlation detection. Digital electronics require many sequential processing steps in which data is shuttled back and forth between the memory and the processing unit. In photonics in-memory computing suitable optical signals are sequentially applied to the memory devices in correspondence to correlations between data streams. The conductance of the devices evolves in accordance with the input, and the result is sequentially retrieved devices. Wavelength multiplexing enables parallel write and read operations on devices.
Two approaches are being independently investigated in the field: The first is to compute at the locations where the data is stored, using the so-called framework of electronic in-memory computing. The second is the idea to compute at the speed of light using optical processing units.
By removing the need to shuttle data between memory and processing units, in-memory computing already brings significant latency reductions. So does optical processing, which in addition to faster data movement leverages property to multiplex photons for parallelized computing.
The holy grail is to combine the two approaches into one, creating in effect a photonic in-memory computing approach. However, this must be achieved in a way where data is communicated as well as processed fully in the optical domain. Together with our collaborators, we developed just that method.
By using the crystallization physics of phase-change materials, we found a way to compute data when it is being transmitted. Think of commuting to work and getting some part of the work done during the commute. This is precisely our approach here. Note that while the interest in optical computing got its start back in the 1960s, the field has recently experienced a renewal with the advent of integrated photonics and functional materials — integrated photonics circuits are already in use for data signaling in data centers.
Detecting temporal correlations in data streams
To demonstrate a real-world use case, we set out to build photonic in-memory computing for solving a computationally difficult task of temporal correlation detection. Correlation detection is a statistical method used for detecting patterns and modeling structures in data streams. Determining the temporal correlation in and between data streams is important for a host of applications, ranging from social media analysis to financial forecasting, the detection of hacking threats, and much more. Importantly, there is no room for latencies in this task since it requires data’s real-time or temporal correlation for computations.
An illustration of our photonic GST cell. The crystalline GST patch is represented in red, with a streak of the amorphous mark. With more and more input crystallization pulses, the crystalline fraction grows within the amorphous volume- a process called accumulative crystallization. In our photonics engine, each event is assigned to a GST cell and with a unique input wavelength for read and write operations. Whenever a spike is detected in an event’s data stream, write pulses are applied to the allocated devices. The sum of all events at any instance is utilized to modulate the width of the write pulses. During the analysis, the transmission in the devices changes, which are used to detect correlations.
We started this work in early 2020, during the COVID pandemic. We first developed a computational algorithm suited for the correlation detection task and tested that out on simulations. Simultaneously, we laid grounds for modeling the device physics of phase change materials and implemented a model with Prof David C. Wright and his group at the University of Exeter. We figured out what format of devices and circuitry we needed and the parameters that would allow us to test our ideas. Over the course of a year, we built and characterized our photonic circuits at the University of Muenster and Oxford in the labs of Prof. Wolfram Pernice and Prof. Harish Bhaskaran.
Crucially, we validated two key new features:
One is the accumulative behavior in phase change material germanium-antimony-tellurium (GST) arising from its crystallization dynamics.
The second is the ability to perform accumulation operations in parallel on multiple photonic phase-change memory devices using the wavelength division multiplexing property of optics. This is an entirely different approach.
The property of phase-change materials that has been so far utilized in the optical domain is the ability to program them to multi-transmissive states for static analog weighing operations. Here, we take an approach where data is directly written into the phase change memory and the computational result is obtained after a data-analysis task is complete.
Broadly, our approach utilizes the crystal growth dynamics in phase change materials to integrate information. Each data stream is assigned a phase change memory device, and a part of the device crystallizes when the light signal has a bit value of 1 in the incoming data bitstream. When more and more device volume crystallizes, the transmission of the optical signal that is read out drops. Thus, devices that end up with reduced transmission after an experiment are the ones that are correlated.
At no point is the incoming data shuttled, or stored. It is both recorded and computed within the same physical phase-change memory device.
By using this property, we demonstrated how correlations in social media data streams and high-traffic data centers can be efficiently detected and recorded. Our photonic engine analyzed over 100,000 tweets to find correlations on the social media platform Twitter, as well as detected anomalies on high-traffic data streams in data centers.
One can elegantly combine our photonic correlator with existing photonic transceivers units, for example, to process data in real-time for the above tasks. Some other demanding applications include data analysis on very long baseline telescopes, and particle accelerators where computations can be performed and recorded during the course of the scientific experiment.
While there are some challenges to work out, such as scaling up our approach, our results set another example of the potential of optics in computing to suggest that integrated photonics is coming of age and in some cases can begin to even challenge electronic computation.
This research was partially funded by European Union’s Horizon 2020 research and innovation program (Fun-COMP project, Grant Number 780848).
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