In-memory photonic dot-product engine with electrically programmable weight banks

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic–electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic–electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ/dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (≥87.36) that leads to an enhanced computing accuracy (standard deviation σ ≤ 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%

Parallel convolution processing using an integrated photonic tensor core

With the proliferation of ultra-high-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence, the world is generating exponentially increasing amounts of data - data that needs to be processed in a fast, efficient and smart way. These developments are pushing the limits of existing computing paradigms, and highly parallelized, fast and scalable hardware concepts are becoming progressively more important. Here, we demonstrate a computational specific integrated photonic tensor core - the optical analog of an ASIC-capable of operating at Tera-Multiply-Accumulate per second (TMAC/s) speeds. The photonic core achieves parallelized photonic in-memory computing using phase-change memory arrays and photonic chip-based optical frequency combs (soliton microcombs). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 GHz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates, ultra-low loss silicon nitride waveguides, and high speed on-chip detectors and modulators, our approach provides a path towards full CMOS wafer-scale integration of the photonic tensor core. While we focus on convolution processing, more generally our results indicate the major potential of integrated photonics for parallel, fast, and efficient computational hardware in demanding AI applications such as autonomous driving, live video processing, and next generation cloud computing services

Broadly-tunable smart glazing using an ultra-thin phase-change material

For many applications, a method for controlling the optical properties of a solid-state film over a broad wavelength range is highly desirable and could have significant commercial impact. One such application is smart glazing technology where it is necessary to harvest near-infrared solar radiation in the winter and reflect it in the summer--an impossibility for materials with fixed thermal and optical properties. Here, we experimentally demonstrate a smart window which uses a thin-film coating containing GeTe, a bi-stable, chalcogenide-based phase-change material which can modulate near-infrared absorption while maintaining neutral-colouration and constant transmission of light at visible wavelengths. We additionally demonstrate controlled down-conversion of absorbed near-infrared energy to far-infrared radiation which can be used to heat a building's interior and show that these thin-films also serve as low-emissivity coatings, reducing heat transfer between a building and its external environment throughout the year. Finally, we demonstrate fast, sub-millisecond switching using transparent electrical heaters integrated on glass substrates. These combined properties result in a smart window that is efficient, affordable, and aesthetically pleasing--three aspects which are crucial for successful adoption of green technology