Training Passive Photonic Reservoirs with Integrated Optical Readout

As Moore's law comes to an end, neuromorphic approaches to computing are on the rise. One of these, passive photonic reservoir computing, is a strong candidate for computing at high bitrates (> 10 Gbps) and with low energy consumption. Currently though, both benefits are limited by the necessity to perform training and readout operations in the electrical domain. Thus, efforts are currently underway in the photonic community to design an integrated optical readout, which allows to perform all operations in the optical domain. In addition to the technological challenge of designing such a readout, new algorithms have to be designed in order to train it. Foremost, suitable algorithms need to be able to deal with the fact that the actual on-chip reservoir states are not directly observable. In this work, we investigate several options for such a training algorithm and propose a solution in which the complex states of the reservoir can be observed by appropriately setting the readout weights, while iterating over a predefined input sequence. We perform numerical simulations in order to compare our method with an ideal baseline requiring full observability as well as with an established black-box optimization approach (CMA-ES).Comment: Accepted for publication in IEEE Transactions on Neural Networks and Learning Systems (TNNLS-2017-P-8539.R1), copyright 2018 IEEE. This research was funded by the EU Horizon 2020 PHRESCO Grant (Grant No. 688579) and the BELSPO IAP P7-35 program Photonics@be. 11 pages, 9 figure

Enhancing the online discovery of geospatial data through taxonomy, folksonomy and semantic annotations

Spatial data infrastructures (SDIs) are meant to facilitate dissemination and consumption of spatial data, amongst others, through publication and discovery of spatial metadata in geoportals. However, geoportals are often known to geoinformation communities only and present technological limitations which make it difficult for general purpose web search engines to discover and index the data catalogued in (or registered with) a geoportal. The mismatch between standard spatial metadata content and the search terms that Web users employ when looking for spatial data, presents a further barrier to spatial data discovery. The need arises for creating and sharing spatial metadata that is discoverable by general purpose web search engines and users alike. Using folksonomies and semantic annotations appears as an option to eliminate the mismatch and to publish the metadata for discovery on the Web. Based on an analysis of search query terms employed when searching for spatial data on the Web, a taxonomy of search terms is constructed. The taxonomy constitutes the basis towards understanding how web resources in general, and HTML pages with standard spatial metadata in particular, can be documented so that they are discoverable by general purpose web search engines. We illustrate the use of the constructed taxonomy in semantic annotation of web resources, such as HTML pages with spatial metadata on the Web

Spatial statistical analysis of dissatisfaction with the performance of local government in the Gauteng City-Region, South Africa

South Africa in general, and the Gauteng City-Region in particular, are grappling with rising service delivery protests and increasing levels of dissatisfaction with government performance. Besides internal government performance measures, citizen satisfaction surveys are useful in providing citizen-based measurement of satisfaction with service delivery and the performance of government. With 27 490 respondents across Gauteng, the 2013 Gauteng City-Region Observatory (GCRO) Quality of Life (QoL) survey provides an interesting snap shot of attitudes towards government. A spatial statistical approach is applied to the 2013 QoL survey data to analyse patterns of dissatisfaction with the performance of local government. The analysis reveals spatial clustering in the level of dissatisfaction with the performance of local government. It also reveals percentage of respondents dissatisfied with dwelling, mean sense of safety index, and percentage agree the country is going in the wrong direction, as significant predictors of the level of local dissatisfaction. Other predictors include the percentage of respondents that think lack of maintenance is the biggest problem facing the community, and percentage agree that politics is waste of time. These results imply the need for incorporating spatial analysis and targeting in the formulation of policy aimed at improving government performance

On-chip passive photonic reservoir computing with integrated optical readout

Photonic reservoir computing is a recent bio-inspired paradigm for signal processing. Despite first successes, the paradigm still faces challenges. We address some of these challenges and introduce our approaches to solve them. In detail, we discuss how integrated reservoirs can be scaled up by injecting multiple copies of the input. Further we introduce a new hardware-friendly training method for integrated optical readouts

A neuromorphic silicon photonics nonlinear equalizer for optical communications with intensity modulation and direct detection

We present the design and numerical study of a nonlinear equalizer for optical communications based on silicon photonics and reservoir computing. The proposed equalizer leverages the optical information processing capabilities of integrated photonic reservoirs to combat distortions both in metro links of a few hundred kilometers and in high-speed short-reach intensity-modulation-direct-detection links. We show nonlinear compensation in unrepeated metro links of up to 200 km that outperform electrical feedforward equalizers based equalizers, and ultimately any linear compensation device. For a high-speed short-reach 40Gb/s link based on a distributed feedback laser and an electroabsorptive modulator, and considering a hard decision forward error correction limit of 0.2 x 10(-2), we can increase the reach by almost 10 km. Our equalizer is compact (only 16 nodes) and operates in the optical domain without the need for complex electronic DSP, meaning its performance is not bandwidth constrained. The approach is, therefore, a viable candidate even for equalization techniques far beyond 100G optical communication links

SPATIAL STATISTICAL ANALYSES TO ASSESS THE SPATIAL EXTENT AND CONCENTRATION OF MULTIDIMENSIONAL POVERTY IN GAUTENG USING THE SOUTH AFRICAN MULTIDIMENSIONAL POVERTY INDEX

Assessment of poverty has generally been carried out using “money-metric” measures. But since poverty is multidimensional, these measures fall short of generating a comprehensive picture of the poor. Contrastingly, multidimensional poverty analyses are capable of generating parameters that help in providing holistic understanding of poverty in its various forms. This study compares two indexes of multidimensional poverty computed from census data collected in 2001 and 2011 in Gauteng (South Africa) by performing a spatial autocorrelation analysis. The results reveal fine-grained detailed variations in the concentration of poverty across the Gauteng province. Overall, multidimensional poverty is concentrated at the periphery of the province while affluence is concentrated in the core urban areas. Pockets of grinding poverty can also be found in core areas juxtaposed with affluence. Such an analysis will lead to the formulation of spatially targeted policy interventions geared towards poverty alleviation

A multiple-input strategy to efficient integrated photonic reservoir computing

Photonic reservoir computing has evolved into a viable contender for the next generation of analog computing platforms as industry looks beyond standard transistor-based computing architectures. Integrated photonic reservoir computing, particularly on the silicon-on-insulator platform, presents a CMOS-compatible, wide bandwidth, parallel platform for implementation of optical reservoirs. A number of demonstrations of the applicability of this platform for processing optical telecommunication signals have been made in the recent past. In this work, we take it a stage further by performing an architectural search for designs that yield the best performance while maintaining power efficiency. We present numerical simulations for an optical circuit model of a 16-node integrated photonic reservoir with the input signal injected in combinations of 2, 4, and 8 nodes, or into all 16 nodes. The reservoir is composed of a network of passive photonic integrated circuit components with the required nonlinearity introduced at the readout point with a photodetector. The resulting error performance on the temporal XOR task for these multiple input cases is compared with that of the typical case of input to a single node. We additionally introduce for the first time in our simulations a realistic model of a photodetector. Based on this, we carry out a full power-level exploration for each of the above input strategies. Multiple-input reservoirs achieve better performance and power efficiency than single-input reservoirs. For the same input power level, multiple-input reservoirs yield lower error rates. The best multiple-input reservoir designs can achieve the error rates of single-input ones with at least two orders of magnitude less total input power. These results can be generally attributed to the increase in richness of the reservoir dynamics and the fact that signals stay longer within the reservoir. If we account for all loss and noise contributions, the minimum input power for error-free performance for the optimal design is found to be in the approximate to 1 mW range