Segmented optical transmitter comprising a CMOS driver array and an InP IQ-MZM for advanced modulation formats

Segmented Mach-Zehnder modulators are promising solutions to generate complex modulation schemes in the migration towards optical links with a higher-spectral efficiency. We present an optical transmitter comprising a segmented-electrode InP IQ-MZM, capable of multilevel optical signal generation (5-bit per I/Q arm) by employing direct digital drive from integrated, low-power (1W) CMOS binary drivers. We discuss the advantages and design tradeoffs of the segmented driver structure and the implementation in a 40 nm CMOS technology. Multilevel operation with combined phase and amplitude modulation is demonstrated experimentally on a single MZM of the device for 2-ASK-2PSK and 4-ASK-2-PSK, showing potential for respectively 16-QAM and 64-QAM modulation in future assemblies

Frequent itemset mining on multiprocessor systems

Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data. Hence, efficient algorithms are required to process such large amounts of data. In recent years, there have been many frequent-itemset mining algorithms proposed, which however (1) often have high memory requirements and (2) do not exploit the large degrees of parallelism provided by modern multiprocessor systems. The high memory requirements arise mainly from inefficient data structures that have only been shown to be sufficient for small datasets. For large datasets, however, the use of these data structures force the algorithms to go out-of-core, i.e., they have to access secondary memory, which leads to serious performance degradations. Exploiting available parallelism is further required to mine large datasets because the serial performance of processors almost stopped increasing. Algorithms should therefore exploit the large number of available threads and also the other kinds of parallelism (e.g., vector instruction sets) besides thread-level parallelism. In this work, we tackle the high memory requirements of frequent itemset mining twofold: we (1) compress the datasets being mined because they must be kept in main memory during several mining invocations and (2) improve existing mining algorithms with memory-efficient data structures. For compressing the datasets, we employ efficient encodings that show a good compression performance on a wide variety of realistic datasets, i.e., the size of the datasets is reduced by up to 6.4x. The encodings can further be applied directly while loading the dataset from disk or network. Since encoding and decoding is repeatedly required for loading and mining the datasets, we reduce its costs by providing parallel encodings that achieve high throughputs for both tasks. For a memory-efficient representation of the mining algorithms’ intermediate data, we propose compact data structures and even employ explicit compression. Both methods together reduce the intermediate data’s size by up to 25x. The smaller memory requirements avoid or delay expensive out-of-core computation when large datasets are mined. For coping with the high parallelism provided by current multiprocessor systems, we identify the performance hot spots and scalability issues of existing frequent-itemset mining algorithms. The hot spots, which form basic building blocks of these algorithms, cover (1) counting the frequency of fixed-length strings, (2) building prefix trees, (3) compressing integer values, and (4) intersecting lists of sorted integer values or bitmaps. For all of them, we discuss how to exploit available parallelism and provide scalable solutions. Furthermore, almost all components of the mining algorithms must be parallelized to keep the sequential fraction of the algorithms as small as possible. We integrate the parallelized building blocks and components into three well-known mining algorithms and further analyze the impact of certain existing optimizations. Our algorithms are already single-threaded often up an order of magnitude faster than existing highly optimized algorithms and further scale almost linear on a large 32-core multiprocessor system. Although our optimizations are intended for frequent-itemset mining algorithms, they can be applied with only minor changes to algorithms that are used for mining of other types of itemsets

High-speed optical data transmission for detector instrumentation in particle physics

This work discusses the advantage of optical transmission utilizing wavelength-division multiplexing for the read-out of experimental data in detector instrumentation in high-energy physics, astroparticle physics or photon science. A multi-channel optical transmitter is developed as the core component on a silicon-on-insulator platform. It implements Mach-Zehnder modulators with a depletion-type pn-phase shifter in each arm, while the (de )multiplexers rely on planar concave gratings. The modulator design is expected to support a symbol rate in the range 40 GBd even with a phase shifter length of 3 mm. The development of an efficient simulation method is presented, which allows for the reliable prediction of the steady-state modulator characteristics. Furthermore, this work addresses the packaging technology for grating-coupled silicon photonic components. In particular, a fabrication and assembly process for a planar fiber-to-chip coupling using angle-polished single-mode fibers is developed. A long-term-stable coupling with a small footprint is achieved, of which the coupling efficiency is only weakly dependent on ambient conditions