Parallel Algorithms for Isolated and Connected Word Recognition

For years researchers have worked toward finding a way to allow people to talk to machines in the same manner a person communicates to another person. This verbal man to machine interface, called speech recognition, can be grouped into three types: isolated word recognition, connected word recognition, and continuous speech recognition. Isolated word recognizers recognize single words with distinctive pauses before and after them. Continuous speech recognizers recognize speech spoken as one person speaks to another, continuously without pauses. Connected word recognition is an extension of isolated word recognition which recognizes groups of words spoken continuously. A group of words must have distinctive pauses before and after it, and the number of words in a group is limited to some small value (typically less than six). If these types of recognition systems are to be successful in the real world, they must be speaker independent and support a large vocabulary. They also must be able to recognize the speech input accurately and in real time. Currently there is no system which can meet all of these criteria because a vast amount of computations are needed. This report examines the use of parallel processing to reduce the computation time for speech recognition. Two different types of parallel architectures are considered here, the Single Instruction stream - Multiple Data (S1MD) machine and the VLSI processor array. The SIMD machine is chosen for its flexibility, which makes it a good candidate for testing new speech recognition algorithms. The VLSI processor array is selected as being good for a dedicated recognition system because of its simple processors and fixed interconnections. This report involves designing SIMD systems and VLSI processor arrays for both isolated and connected word recognition systems. These architectures are evaluated and contrasted in terms of the number of processors needed, the interprocessor connections required, and the “power” each processor needs to achieve real time recognition. The results show that an SIMD machine using 100 processors, each with an MC68000 processor, can recognize isolated words in real time using a 20 KHz sampling rate and a 1,000 word vocabulary

Accelerating Time Series Analysis via Processing using Non-Volatile Memories

Time Series Analysis (TSA) is a critical workload for consumer-facing devices. Accelerating TSA is vital for many domains as it enables the extraction of valuable information and predict future events. The state-of-the-art algorithm in TSA is the subsequence Dynamic Time Warping (sDTW) algorithm. However, sDTW's computation complexity increases quadratically with the time series' length, resulting in two performance implications. First, the amount of data parallelism available is significantly higher than the small number of processing units enabled by commodity systems (e.g., CPUs). Second, sDTW is bottlenecked by memory because it 1) has low arithmetic intensity and 2) incurs a large memory footprint. To tackle these two challenges, we leverage Processing-using-Memory (PuM) by performing in-situ computation where data resides, using the memory cells. PuM provides a promising solution to alleviate data movement bottlenecks and exposes immense parallelism. In this work, we present MATSA, the first MRAM-based Accelerator for Time Series Analysis. The key idea is to exploit magneto-resistive memory crossbars to enable energy-efficient and fast time series computation in memory. MATSA provides the following key benefits: 1) it leverages high levels of parallelism in the memory substrate by exploiting column-wise arithmetic operations, and 2) it significantly reduces the data movement costs performing computation using the memory cells. We evaluate three versions of MATSA to match the requirements of different environments (e.g., embedded, desktop, or HPC computing) based on MRAM technology trends. We perform a design space exploration and demonstrate that our HPC version of MATSA can improve performance by 7.35x/6.15x/6.31x and energy efficiency by 11.29x/4.21x/2.65x over server CPU, GPU and PNM architectures, respectively

Tuning the Computational Effort: An Adaptive Accuracy-aware Approach Across System Layers

This thesis introduces a novel methodology to realize accuracy-aware systems, which will help designers integrate accuracy awareness into their systems. It proposes an adaptive accuracy-aware approach across system layers that addresses current challenges in that domain, combining and tuning accuracy-aware methods on different system layers. To widen the scope of accuracy-aware computing including approximate computing for other domains, this thesis presents innovative accuracy-aware methods and techniques for different system layers. The required tuning of the accuracy-aware methods is integrated into a configuration layer that tunes the available knobs of the accuracy-aware methods integrated into a system

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

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

On the synthesis and processing of high quality audio signals by parallel computers

This work concerns the application of new computer architectures to the creation and manipulation of high-quality audio bandwidth signals. The configuration of both the hardware and software in such systems falls under consideration in the three major sections which present increasing levels of algorithmic concurrency. In the first section, the programs which are described are distributed in identical copies across an array of processing elements; these programs run autonomously, generating data independently, but with control parameters peculiar to each copy: this type of concurrency is referred to as isonomic}The central section presents a structure which distributes tasks across an arbitrary network of processors; the flow of control in such a program is quasi- indeterminate, and controlled on a demand basis by the rate of completion of the slave tasks and their irregular interaction with the master. Whilst that interaction is, in principle, deterministic, it is also data-dependent; the dynamic nature of task allocation demands that no a priori knowledge of the rate of task completion be required. This type of concurrency is called dianomic? Finally, an architecture is described which will support a very high level of algorithmic concurrency. The programs which make efficient use of such a machine are designed not by considering flow of control, but by considering flow of data. Each atomic algorithmic unit is made as simple as possible, which results in the extensive distribution of a program over very many processing elements. Programs designed by considering only the optimum data exchange routes are said to exhibit systolic^ concurrency. Often neglected in the study of system design are those provisions necessary for practical implementations. It was intended to provide users with useful application programs in fulfilment of this study; the target group is electroacoustic composers, who use digital signal processing techniques in the context of musical composition. Some of the algorithms in use in this field are highly complex, often requiring a quantity of processing for each sample which exceeds that currently available even from very powerful computers. Consequently, applications tend to operate not in 'real-time' (where the output of a system responds to its input apparently instantaneously), but by the manipulation of sounds recorded digitally on a mass storage device. The first two sections adopt existing, public-domain software, and seek to increase its speed of execution significantly by parallel techniques, with the minimum compromise of functionality and ease of use. Those chosen are the general- purpose direct synthesis program CSOUND, from M.I.T., and a stand-alone phase vocoder system from the C.D.P..(^4) In each case, the desired aim is achieved: to increase speed of execution by two orders of magnitude over the systems currently in use by composers. This requires substantial restructuring of the programs, and careful consideration of the best computer architectures on which they are to run concurrently. The third section examines the rationale behind the use of computers in music, and begins with the implementation of a sophisticated electronic musical instrument capable of a degree of expression at least equal to its acoustic counterparts. It seems that the flexible control of such an instrument demands a greater computing resource than the sound synthesis part. A machine has been constructed with the intention of enabling the 'gestural capture' of performance information in real-time; the structure of this computer, which has one hundred and sixty high-performance microprocessors running in parallel, is expounded; and the systolic programming techniques required to take advantage of such an array are illustrated in the Occam programming language