Hypermultiplexed integrated-photonics-based optical tensor processor

The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), internet of things (IoT), and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware. Here we demonstrate a hypermultiplexed integrated photonics-based tensor optical processor (HITOP) that can perform trillions of operations per second (TOPS) at the energy efficiency of 40 TOPS/W. Space-time-wavelength three-dimensional (3D) optical parallelism enables O(N2) operations per clock-cycle using O(N) modulator devices. The system is built with wafer-fabricated III/V micron-scale lasers and high-speed thin-film Lithium-Niobate electro-optics for encoding at 10s femtojoule/symbol. Lasing threshold incorporates an analog inline rectifier (ReLu) nonlinearity for low-latency activation. The system scalability is verified with machine learning models of 405,000 parameters. A combination of high clockrates, energy-efficient processing, and progra..., , , # Hypermultiplexed integrated-photonics-based optical tensor processor

[https://doi.org/10.5061/dryad.kprr4xhgj](https://doi.org/10.5061/dryad.kprr4xhgj)

This repo shares the data used to produce the figures in the journal paper Hypermultiplexed Integrated-Photonics-based Optical Tensor Processor (link: `https://arxiv.org/abs/2401.18050`). The code for data analysis is also provided. For more clear description of the data, please check our paper.

## Description of the data and file structure

* All the data and code are categorized by the sequence of figures; each compressed file is named by the corresponding figure and contains all the data used to draw the corresponding figure.
* **Notice: Be careful when accessing files inside, all the data processing code is well-tuned by the path of each file. If there is a file missing, it would cause errors when re-processing the data**
* The file structure is as follows, we left out the tree for some of the files (data of the inference, etc)....,