In-memory computing on a photonic platform
Carlos R\'ios, Nathan Youngblood, Zengguang Cheng, Manuel Le Gallo,, Wolfram H.P. Pernice, C David Wright, Abu Sebastian, Harish Bhaskaran

TL;DR
This paper demonstrates an all-photonic in-memory computing platform using integrated optics and phase-change materials, enabling direct scalar multiplication with potential for fully photonic computing systems.
Contribution
It introduces a novel all-photonic in-memory computation method using phase-change materials for scalar multiplication, eliminating the need for electronic conversions.
Findings
Achieved direct scalar multiplication with phase-change photonic elements.
Developed a single-shot Write/Erase process that is drift-free.
Showcased the potential for fully photonic computing architectures.
Abstract
Collocated data processing and storage are the norm in biological systems. Indeed, the von Neumann computing architecture, that physically and temporally separates processing and memory, was born more of pragmatism based on available technology. As our ability to create better hardware improves, new computational paradigms are being explored. Integrated photonic circuits are regarded as an attractive solution for on-chip computing using only light, leveraging the increased speed and bandwidth potential of working in the optical domain, and importantly, removing the need for time and energy sapping electro-optical conversions. Here we show that we can combine the emerging area of integrated optics with collocated data storage and processing to enable all-photonic in-memory computations. By employing non-volatile photonic elements based on the phase-change material, Ge2Sb2Te5, we are able…
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