Coherent photonic crossbar as a universal linear operator

Linear optics aim at realizing any real- and/or complex-valued matrix operator via optical elements, addressing a broad field of applications in the areas of quantum photonics, microwave photonics and optical neural networks. The transfer of linear operators into photonic experimental layouts typically relies on Singular Value Decomposition (SVD) techniques combining meshes of cascaded 2x2 Mach Zehnder Interferometers (MZIs), with the main challenges being the precision in the experimental representation of the targeted matrix, referred to as fidelity, and the overall insertion loss. We demonstrate a novel interferometric coherent photonic crossbar architecture (Xbar) that demarcates from state-of-the-art SVD implementations and can realize any linear operator, supporting full restoration of the loss-induced fidelity. Its novel interferometric design allows for the direct mapping of each matrix element to a single, designated Xbar node, bringing down the number of programming steps to only one. We present the theoretical foundations of the Xbar, proving that its insertion losses scale linearly with the node losses as opposed to the exponential scaling witnessed by the SVD counterparts. This leads to a matrix design with significantly lower overall insertion losses compared to SVD-based schemes when utilizing state-of-the-art silicon photonic fabrication metrics, allowing for alternative node technologies with lower energy consumption and higher operational speed credentials to be employed. Finally, we validate that our Xbar architecture is the first linear operator that supports fidelity restoration, outperforming SVD schemes in loss- and phase-error fidelity performance and forming a significantly more robust layout to loss and phase deviations

Self-Seeded RSOA-Fiber Cavity Lasers vs. ASE Spectrum-Sliced or Externally Seeded Transmitters—A Comparative Study

Reflective semiconductor optical amplifier fiber cavity lasers (RSOA-FCLs) are appealing, colorless, self-seeded, self-tuning and cost-efficient upstream transmitters. They are of interest for wavelength division multiplexed passive optical networks (WDM-PONs) based links. In this paper, we compare RSOA-FCLs with alternative colorless sources, namely the amplified spontaneous emission (ASE) spectrum-sliced and the externally seeded RSOAs. We compare the differences in output power, signal-to-noise ratio (SNR), relative intensity noise (RIN), frequency response and transmission characteristics of these three sources. It is shown that an RSOA-FCL offers a higher output power over an ASE spectrum-sliced source with SNR, RIN and frequency response characteristics halfway between an ASE spectrum-sliced and a more expensive externally seeded RSOA. The results show that the RSOA-FCL is a cost-efficient WDM-PON upstream source, borrowing simplicity and cost-efficiency from ASE spectrum slicing with characteristics that are, in many instances, good enough to perform short-haul transmission. To substantiate our statement and to quantitatively compare the potential of the three schemes, we perform data transmission experiments at 5 and 10 Gbit/s

Neuromorphic silicon photonics and hardware-aware deep learning for high-speed inference

The relentless growth of Artificial Intelligence (AI) workloads has fueled the drive towards non-Von Neuman architectures and custom computing hardware. Neuromorphic photonic engines aspire to synergize the low-power and high-bandwidth credentials of light-based deployments with novel architectures, towards surpassing the computing performance of their electronic counterparts. In this paper, we review recent progress in integrated photonic neuromorphic architectures and analyze the architectural and photonic hardware-based factors that limit their performance. Subsequently, we present our approach towards transforming silicon coherent neuromorphic layouts into high-speed and high-accuracy Deep Learning (DL) engines by combining robust architectures with hardware-aware DL training. Circuit robustness is ensured through a crossbar layout that circumvents insertion loss and fidelity constraints of state-of-the-art linear optical designs. Concurrently, we employ DL training models adapted to the underlying photonic hardware, incorporating noise- and bandwidth-limitations together with the supported activation function directly into Neural Network (NN) training. We validate experimentally the high-speed and high-accuracy advantages of hardware-aware DL models when combined with robust architectures through a SiPho prototype implementing a single column of a 4:4 photonic crossbar. This was utilized as the pen-ultimate hidden layer of a NN, revealing up to 5.93% accuracy improvement at 5GMAC/sec/axon when noise-aware training is enforced and allowing accuracies of 99.15% and 79.8% for the MNIST and CIFAR-10 classification tasks. Channel-aware training was then demonstrated by integrating the frequency response of the photonic hardware in NN training, with its experimental validation with the MNIST dataset revealing an accuracy increase of 12.93% at a record-high rate of 25GMAC/sec/axon