A multi-level breakthrough in optical computing—a faster, more efficient, and robust memory cell

Amazing stuff!

"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. In the article, 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 authors propose a resonance-based photonic architecture which leverages the non-reciprocal phase shift in magneto-optical materials to implement photonic in-memory computing. ...

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. ...

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. ..."

From the abstract:

"Processing information in the optical domain promises advantages in both speed and energy efficiency over existing digital hardware for a variety of emerging applications in artificial intelligence and machine learning. 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 an array of photonic memory cells is currently limited by a wide range of material- and device-level issues, such as the programming speed, extinction ratio and endurance, among others. Here we propose a new approach to encoding optical weights for in-memory photonic computing using magneto-optic memory cells comprising heterogeneously integrated cerium-substituted yttrium iron garnet (Ce:YIG) on silicon micro-ring resonators. We show that leveraging the non-reciprocal phase shift in such magneto-optic materials offers several key advantages over existing architectures, providing a fast (1 ns), efficient (143 fJ per bit) and robust (2.4 billion programming cycles) platform for on-chip optical processing."

A multi-level breakthrough in optical computing—a faster, more efficient, and robust memory cell "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."

A Multi-Level Breakthrough in Optical Computing (original news release) "Engineers at Pitt, UC Santa Barbara, University of Cagliari, and Institute of Science Tokyo Demonstrate Faster, More Efficient, and Robust Memory Cell"

Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing (open access)

Fig. 1: Non-reciprocal photonic in-memory computing.