Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators

Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix-vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon nitride platforms suited for extracting feature maps in convolutional neural networks. The reduction in bit resolution when crossing the processor is mainly due to optical losses, in the range 2.3-3.3 for the silicon-on-insulator chip and in the range 1.3-2.4 for the silicon nitride chip. However, the lower extinction ratio of Mach-Zehnder elements in the latter platform limits their expressivity (i.e., the capacity to implement any transformation) to 75%, compared to 97% of the former. Finally, the silicon-on-insulator processor outperforms the silicon nitride one in terms of footprint and energy efficiency

Addressing the programming challenges of practical interferometric mesh based optical processors

We demonstrate a novel mesh of Mach-Zehnder interferometers (MZIs) for programmable optical processors. The proposed mesh, referred to as Bokun mesh, is an architecture that merges the attributes of the prior topologies Diamond and Clements. Similar to Diamond, Bokun provides diagonal paths passing through every individual MZI enabling direct phase monitoring. However, unlike Diamond and similar to Clements, Bokun maintains a minimum optical depth leading to better scalability. Providing the monitoring option, Bokun's programming is faster improving the total energy efficiency of the processor. The performance of Bokun mesh enabled by an optimal optical depth is also more resilient to the loss and fabrication imperfections compared to architectures with longer depth such as Reck and Diamond. Employing an efficient programming scheme, the proposed architecture improves energy efficiency by 83% maintaining the same computation accuracy for weight matrix changes at 2 kHz

Breakthroughs in Photonics 2014: Optical Interconnection Networks

This paper highlights the 2014 breakthroughs in the area of optical interconnection networks. As photonic components are being further integrated, particular attention is given to integrated optical interconnection networks for modern computer systems, beyond optical point-to-point interconnectivity. This field of research has become increasingly multidisciplinary with impressive system integration demonstrations

On-chip Optical Phase Monitoring in Multi-Transverse-Mode Integrated Silicon-based Optical Processors

We design a Multi-Transverse-Mode Optical Processor (MTMOP) on 220 nm thick Silicon Photonics exploiting the first two quasi-transverse electric modes (TE0 and TE1). The objective is to measure the optical phase, required for programming the optical processor, without use of conventional optical phase detection techniques (e.g., coherent detection). In the proposed design, we use a novel but simple building block that converts the optical phase to optical power. Mode TE0 carries the main optical signal while mode TE1 is for programming purposes. The MTMOP operation relies on the fact that the group velocity of TE0 and TE1 propagating through a mode-sensitive phase shifter are different. An unbalanced Mach-Zehnder interferometer (MZI) consists of a mode-sensitive and mode-insensitive phase shifters in the two arms. We set the bias of the phase shifters so that TE0 propagating in the two arms constructively interfere while this will not be the case for TE1. Hence, we detect the phase shift applied to TE0 by measuring the variation in the optical power of TE1. To the best of our knowledge, this design is the first attempt towards realizing a programmable optical processor with fully integrated programming unit exploiting multimode silicon photonics