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A new method for photonic in-memory computing

12.11.2024 - New approach to combine non-volatility, multibit storage, high switching speed and low switching energy.

For the first time, an international cadre of electrical engineers has developed a new method for photonic in-memory computing that could make optical computing a reality in the near future. The team includes researchers from the University of Pittsburgh Swanson School of Engineering, the University of California Santa Barbara, the University of Cagliari, and the Tokyo Institute of Technology. 

Until now, researchers have been limited in developing photonic memory for AI processing – gaining one important attribute like speed while sacrificing another like energy usage. Now, the inter­national team demonstrates a unique solution that addresses current limitations of optical memory that have yet to combine non-volati­lity, multibit storage, high switching speed, low switching energy, and high endurance in a single platform.

“The materials we use in developing these cells have been available for decades. However, they have primarily been used for static optical applications, such as on-chip isolators rather than a platform for high performance photonic memory,” Nathan Youngblood from the University of Pittsburgh explained. “This discovery is a key enabling techno­logy toward a faster, more efficient, and more scalable optical computing architecture that can be directly programmed with CMOS circuitry – which means it can be integrated into today’s computer technology. Addi­tionally, our technology showed three orders of magnitude better endurance than other non-volatile approaches, with 2.4 billion switching cycles and nanosecond speeds.”

The scientists propose a resonance-based photonic architecture which leverages the non-reciprocal phase shift in magneto-optical materials to implement photonic in-memory computing. A typical approach to photonic processing is to multiply a rapidly changing optical input vector with a matrix of fixed optical weights. However, encoding these weights on-chip using traditional methods and materials has proven challenging. By using magneto-optic memory cells comprised of heterogeneously integrated cerium-substituted yttrium iron garnet (Ce:YIG) on silicon micro-ring resonators, the cells cause light to propagate bidirec­tionally, like sprinters running opposite directions on a track.

“It’s like the wind is blowing against one sprinter while helping the other run faster,” explained Paulo Pintus, who led the experimental work at UC Santa Barbara. “By applying a magnetic field to the memory cells, we can control the speed of light differently depending on whether the light is flowing clockwise or counter­clockwise around the ring resonator. This provides an additional level of control not possible in more conventional non-magnetic materials.”

The team is now working to scale up from a single memory cell to a large-scale memory array which can support even more data for computing applications. They note in the article that the non-reciprocal magneto-optic memory cell offers an efficient non-volatile storage solution that could provide unlimited read/write endurance at sub-nano­second pro­gramming speeds. “We also believe that future advances of this technology could use different effects to improve the switching efficiency,” Yuya Shoji at the Institute of Science Tokyo added, “and that new fabri­cation techniques with materials other than Ce:YIG and more precise deposition can further advance the potential of non-reciprocal optical computing.” (Source: U. Pittsburgh)

Reference: P. Pintus et al.: Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing, Nat. Photon., online 23 October 2024; DOI: 10.1038/s41566-024-01549-1

Link: Youngblood Photonics Lab, Dept. of Electrical and Computer Engineering, Univ. of Pittsburgh, Pittsburgh, USA

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Digital tools or software can ease your life as a photonics professional by either helping you with your system design or during the manufacturing process or when purchasing components. Check out our compilation:

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