An efficient implementation of lattice-ladder multilayer perceptrons in field programmable gate arrays

The implementation efficiency of electronic systems is a combination of conflicting requirements, as increasing volumes of computations, accelerating the exchange of data, at the same time increasing energy consumption forcing the researchers not only to optimize the algorithm, but also to quickly implement in a specialized hardware. Therefore in this work, the problem of efficient and straightforward implementation of operating in a real-time electronic intelligent systems on field-programmable gate array (FPGA) is tackled. The object of research is specialized FPGA intellectual property (IP) cores that operate in a real-time. In the thesis the following main aspects of the research object are investigated: implementation criteria and techniques. The aim of the thesis is to optimize the FPGA implementation process of selected class dynamic artificial neural networks. In order to solve stated problem and reach the goal following main tasks of the thesis are formulated: rationalize the selection of a class of Lattice-Ladder Multi-Layer Perceptron (LLMLP) and its electronic intelligent system test-bed – a speaker dependent Lithuanian speech recognizer, to be created and investigated; develop dedicated technique for implementation of LLMLP class on FPGA that is based on specialized efficiency criteria for a circuitry synthesis; develop and experimentally affirm the efficiency of optimized FPGA IP cores used in Lithuanian speech recognizer. The dissertation contains: introduction, four chapters and general conclusions. The first chapter reveals the fundamental knowledge on computer-aideddesign, artificial neural networks and speech recognition implementation on FPGA. In the second chapter the efficiency criteria and technique of LLMLP IP cores implementation are proposed in order to make multi-objective optimization of throughput, LLMLP complexity and resource utilization. The data flow graphs are applied for optimization of LLMLP computations. The optimized neuron processing element is proposed. The IP cores for features extraction and comparison are developed for Lithuanian speech recognizer and analyzed in third chapter. The fourth chapter is devoted for experimental verification of developed numerous LLMLP IP cores. The experiments of isolated word recognition accuracy and speed for different speakers, signal to noise ratios, features extraction and accelerated comparison methods were performed. The main results of the thesis were published in 12 scientific publications: eight of them were printed in peer-reviewed scientific journals, four of them in a Thomson Reuters Web of Science database, four articles – in conference proceedings. The results were presented in 17 scientific conferences

Harmony Analysis in A’Capella Singing

Speech production is made by the larynx and then modified by the articulators; this speech contains large amounts of useful information. Similar to speech, singing is made by the same method; albeit with a specific acoustic difference; singing contains rhythm and is usually of a higher intensity. Singing is almost always accompanied by musical instruments which generally makes detecting and separating voice difficult (Kim Hm 2012). A’ Capella singing is known for singing without musical accompaniment, making it somewhat easier to retrieve vocal information. The methods developed to detect information from speech are not new concepts and are commonly applied to almost every item in the average household. Singing processing adapts a large portion of these techniques to detect vocal information of singers including melody, language, emotion, harmony and pitch. The techniques used in speech and singing processing are catagorised into one of three categories: 1. Time Domain 2. Frequency Domain 3. Other Algorithms This project will utilise an algorithm from each category; In particular, Average Magnitude Difference Function (AMDF), Cepstral Analysis and Linear Predictive Coding (LPC). AMDF is the result of taking the absolute value of a sample taken a time (k) and a delayed version of itself at (k-n). Its known to provide relatively good accuracy with low computational cost, however it is prone to variation in background noise (Hui, L et al 2006). Cepstral Analysis is known for separating the convolved version of a signal into the source and voice tract components and provides fast computational speeds from utilising the ii Fourier Transform and its Inverse. LPC provides a linear estimation of past values of a signal, the resulting predictor and error coefficients are utilised to develop the spectral envelope for pitch detection. The project tested the algorithms against 11 tracks containing different harmonic content, each method was compared on their speed, accuracy, where applicable the number of notes correctly identified. All three algorithms gave relatively good results against single note tracks, with the LPC algorithms providing the most accurate results. When tested against multi-note tracks and pre-recorder singing tracks the AMDF and Cepstral Analysis methods performed poorly in terms of the accuracy and number of correctly identified notes. LPC method performed considerably better returning an average of 66.8% of notes correctly

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp