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Register a new userSimphony: An open-source photonic integrated circuit simulation framework
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We present Simphony, a free and open-source software toolbox for abstracting and simulating photonic integrated circuits, implemented in Python. The toolbox is both fast and easily extensible; plugins can be written to provide compatibility with existing layout tools, and device libraries can be easily created without a deep knowledge of programming. We include several examples of photonic circuit simulations with novel features and demonstrate a speedup of more than 20x over a leading commercially available software tool.
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Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using these Resistive Random-Access Memory (RRAM) devices can be used to efficiently implement various in-memory computing operations, such as Multiply Accumulate (MAC) and unrolled-convolutions, which are used extensively in Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). However, memristive devices face concerns of aging and non-idealities, which limit the accuracy, reliability, and robustness of Memristive Deep Learning Systems (MDLSs), that should be considered prior to circuit-level realization. This Original Software Publication (OSP) presents MemTorch, an open-source framework for customized large-scale memristive DL simulations, with a refined focus on the co-simulation of device non-idealities. MemTorch also facilitates co-modelling of key crossbar peripheral circuitry. MemTorch adopts a modernized soft-ware engineering methodology and integrates directly with the well-known PyTorch Machine Learning (ML) library
There has been a recent surge of interest in the implementation of linear operations such as matrix multipications using photonic integrated circuit technology. However, these approaches require an efficient and flexible way to perform nonlinear operations in the photonic domain. We have fabricated an optoelectronic nonlinear device--a laser neuron--that uses excitable laser dynamics to achieve biologically-inspired spiking behavior. We demonstrate functionality with simultaneous excitation, inhibition, and summation across multiple wavelengths. We also demonstrate cascadability and compatibility with a wavelength multiplexing protocol, both essential for larger scale system integration. Laser neurons represent an important class of optoelectronic nonlinear processors that can complement both the enormous bandwidth density and energy efficiency of photonic computing operations.
A photonic integrated circuit comprised of an 11 cm multimode speckle waveguide, a 1x32 splitter, and a linear grating coupler array is fabricated and utilized to receive 2 GHz of RF signal bandwidth from 2.5 to 4.5 GHz using a 35 MHz mode locked laser.
Random number generators are essential to ensure performance in information technologies, including cryptography, stochastic simulations and massive data processing. The quality of random numbers ultimately determines the security and privacy that can be achieved, while the speed at which they can be generated poses limits to the utilisation of the available resources. In this work we propose and demonstrate a quantum entropy source for random number generation on an indium phosphide photonic integrated circuit made possible by a new design using two-laser interference and heterodyne detection. The resulting device offers high-speed operation with unprecedented security guarantees and reduced form factor. It is also compatible with complementary metal-oxide semiconductor technology, opening the path to its integration in computation and communication electronic cards, which is particularly relevant for the intensive migration of information processing and storage tasks from local premises to cloud data centres.
We propose an optimization method to improve power efficiency and robustness in silicon-photonic-based coherent integrated photonic neural networks. Our method reduces the network power consumption by 15.3% and the accuracy loss under uncertainties by 16.1%.
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