Στο πλαίσιο αυτής της εργασίας μελετήθηκαν οι διατάξεις επεξεργαστών και πιο συγκεκριμένα οι συστολικές διατάξεις επεξεργαστών. Επίσης αναπτύχθηκε CAD εργαλείο για την αυτόματη VLSI σχεδίαση συστολικών διατάξεων επεξεργαστών για αλγόριθμους επεξεργασίας σήματος. / In this book were studied the processor arrays and more concretely the systolic processor arrays. Also was developed CAD tool for the automatic VLSI designing of systolic processor arrays on signal processing algorithms.

The focus of this thesis is on the design and selection of systolic architectures for ASIC implementation of the real-time digital signal processing task of Modi- fied Covariance spectral estimation. When used with pulsed Doppler ultrasound blood flow detectors, the Modified Covariance spectral estimator offers increased sensitivity in the detection of arterial disease over conventional Fourier transform based methods. The systolic model of computation is considered because through pipelining and parallel processing high levels of concurrency can be achieved to attain the nec- essary throughput for real-time operation. Systolic arrays of simple processing units are also well suited for implementation on VLSI. The versatility of the de- sign of systolic arrays using the rigorous data dependence graph methodology is demonstrated throughout the thesis by application to all sections of the spectral estimator design at both word and bit levels. Systolic array design for the model order 4 Modified Covariance spectral estima- tor, known to offer accurate estimation of blood flow mean velocity and d1stur- bance at an acceptable computational burden, is initially discussed. A variety of problem size dependent systolic arrays for real-time implementation of the fixed model order spectral estimator are designed using data dependence graph mapping methods. Optimal designs are chosen by comparison of hardware, com- munication and control costs, as well as efficiency, timing, data flow and accuracy considerations. A cost/benefit analysis, based on results from structural simula- tion of the arrays, allows the most suitable word-lengths to be chosen. Problem size independent systolic arrays are then discussed as means of coping with the huge increases in computational burden for a Modified Covariance spec- tral estimator which is programmable up to high model orders. This type of array can be used to reduce the number of PEs and increase efficiency when compared to the problem size dependent arrays and the research culminates in the proposal of a novel spiral systolic array for Cholesky decomposition.

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International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / This paper describes the development of a Reed-Solomon (RS) Encoder-Decoder which implements the RS segment of the telemetry channel coding scheme recommended by the Consultative Committee on Space Data Systems (CCSDS)[1]. The Euclidean algorithm has been chosen for the decoder implementation, the hardware realization taking a systolic array approach. The fully pipelined decoder runs on a single clock and the operating speed is limited only by the Galois Field (GF) multiplier's delay. The circuit has been synthesised from VHDL descriptions and the hardware is being realised using programmable logic chips. This circuit was simulated for functional operation and found to perform correction of error patterns exactly as predicted by theory.

Data mining plays an important role in a variety of fields including bioinformatics, multimedia, business intelligence, marketing, and medical diagnosis. Analysis of today’s huge and complex data involves several data mining algorithms including clustering and classification. The computational complexity of machine learning and data mining algorithms, that are frequently used in today’s applications such as embedded systems, makes the design of efficient hardware architectures for these algorithms a challenging issue for the development of such systems. The aim of this work is to optimize the performance of hardware acceleration for data mining applications in terms of speed and area. Most of the previous accelerator architectures proposed in the literature have been obtained using ad hoc techniques that do not allow for design space exploration, some did not consider the size (number of samples) and dimensionality (number of features in each sample) of the datasets. To obtain practical architectures that are amenable for hardware implementation, size and dimensionality of input datasets are taken into consideration in this work. For one-dimensional data, algorithm-level optimizations are investigated to design a fast and area-efficient hardware accelerator for clustering one-dimensional datasets using the well-known K-Means clustering algorithm. Experimental results show that the optimizations adopted in the proposed architecture result in faster convergence of the algorithm using less hardware resources while maintaining the quality of clustering results. The computation of similarity distance matrices is one of the computational kernels that are generally required by several machine learning and data mining algorithms to measure the degree of similarity between data samples. For these algorithms, distance calculation is considered a computationally intensive task that accounts for a significant portion of the processing time. A systematic methodology is presented to explore the design space of 2-D and 1-D processor array architectures for similarity distance computation involved in processing datasets of different sizes and dimensions. Six 2-D and six 1-D processor array architectures are developed systematically using linear scheduling and projection operations. The obtained architectures are classified based on the size and dimensionality of input datasets, analyzed in terms of speed and area, and compared with previous architectures in the literature. Motivated by the necessity to accommodate large-scale and high-dimensional data, nonlinear scheduling and projection operations are finally introduced to design a scalable processor array architecture for the computation of similarity distance matrices. Implementation results of the proposed architecture show improved compromise between area and speed. Moreover, it scales better for large and high-dimensional datasets since the architecture is fully parameterized and only has to deal with one data dimension in each time step. / Graduate / 2019-12-31

The integral image representation of an image is important for a large number of modern image processing algorithms. Integral image representations can reduce computation and increase the operating speed of certain algorithms, improving real-time performance. Due to increasing demand for real-time image processing performance, an integral image architecture capable of accelerating the calculation based on the amount of available resources is presented. Use of the proposed accelerator allows for subsequent stages of a design to have data sooner and execute in parallel. It is shown here how, with some additional resources used in the Field Programmable Gate Array (FPGA), a speed increase is obtained by using a one-dimensional Systolic Array (SA) approach. Additionally, extra guidelines are given for further research in this area.

Brainstem Auditory Evoked Potentials (BAEP) contain valuable information about the condition of the neural fibers associated with the auditory pathways. Extraction of this information is a difficult task due to contamination by on-going scalp EEG. This thesis reviews the current processing techniques and introduces adaptive noise cancellation (ANC) using systolic arrays as an alternative to existing technology. Q-R decomposition theory is reviewed and an explanation of the mechanics of systolic adaptive noise cancellation (SANC) is presented. A modified Given's rotation algorithm is derived resulting in a saving of up to 2/3 in memory requirements. Real data were collected in the laboratory. Real and simulated data were processed to determine the characteristics and effectiveness of adaptive noise cancellation strategies. Successful ANC of BAEP was performed on simulated data using a number or signal-to-noise ratios (S/N), data sequence lengths, reference signals and filter parameter values. We conclude that systolic arrays are a very powerful and appropriate technique for the extraction or BAEPs. Correlation studies indicated that the pre-stimulus EEG signal is inadequately correlated to the primary signal for successful ANC or BAEP in real data. A multi-channel collection scheme is outlined for future collection or Evoked Potential data. A summary or experimental results is presented to address the problem or data collection and signal processing optimization. / Thesis / Master of Engineering (MEngr)

In this study, delay-insensitive asynchronous circuit design style has been adopted to systolic array architectures to exploit the benefits of both techniques for improved throughput. A delay-insensitivity verification analysis method employing symbolic delays is proposed for bit-level pipelined asynchronous circuits. The proposed verification method allows datadependent early output evaluation to co-exist with robust delay-insensitive circuit behavior in pipelined architectures such as systolic arrays. Regardless of the length of the pipeline, delay-insensitivity verification of a systolic array with early output evaluation paths in onedimension is reduced to analysis of three adjacent systoles for eight possible early/late output evaluation scenarios. Analyzing both combinational and sequential parts concurrently, delay-insensitivity violations are located and corrected at structural level, without diminishing the early output evaluation benefits. Since symbolic delays are used without imposing any timing constraints on the environment / the method is technology independent and robust against all physical and environmental variations. To demonstrate the verification method, adders are selected for being at the core of data processing systems. Two asynchronous adder topologies in the delay-insensitive dual-rail threshold logic style, having data-dependent early carry evaluation paths, are converted into bit-level pipelined systolic arrays. On these adders, data-dependent delay-insensitivity violations are detected and resolved using the proposed verification technique. The modified adders achieved the targeted O(log2n) average completion time and -as a result of bit-level pipelining- nearly constant throughput against increased bit-length. The delay-insensitivity verification method could further be extended to handle more early output evaluation paths in multi-dimension.

A Field Programmable Gate Array (FPGA)-based Polymorphic Faddeev Systolic Array (PolyFSA) architecture is proposed to accelerate an Extended Kalman Filter (EKF) algorithm. A system architecture comprising a software processor as the host processor, a hardware controller, a cache-based memory sub-system, and the proposed PolyFSA as co-processor, is presented. PolyFSA-based system architecture is implemented on a Xilinx Virtex 4 family of FPGAs. Results indicate significant speed-ups for the proposed architecture when compared against a space-based software processor. This dissertation proposes a comprehensive architecture analysis that is comprised of (i) error analysis, (ii) performance analysis, and (iii) area analysis. Results are presented in the form of 2-D pareto plots (area versus error, area versus time) and a 3-D plot (area versus time versus error). These plots indicate area savings obtained by varying any design constraints for the PolyFSA architecture. The proposed performance model can be reused to estimate the execution time of EKF on other conventional hardware architectures. In this dissertation, the performance of the proposed PolyFSA is compared against the performance of two conventional hardware architectures. The proposed architecture outperforms the other two in most test cases.

Deep neural networks (DNNs) are extensively used for inference in a wide range of emerging mobile and edge application domains, including autonomous vehicles, drones, augmented and virtual reality (AR/VR), etc. Due to the increasing popularity of these applications, there has been an increasing demand for mobile/edge DNN accelerators to achieve low inference latency and high efficiency. Furthermore, these mobile/edge applications also need to execute multi-DNN workloads, where multiple independent DNNs execute subtasks to complete one large task. This thesis aims to optimize the efficiency of systolic arrays for DNN acceleration because they are among the most popular architectures for DNN inference in mobile/edge systems due to their straightforward design and dataflow. Systolic arrays provide several degrees of freedom to co-optimize performance, power, area, and temperature–namely, die/chiplet architecture (number of processing elements, on-chip memory capacity and its architecture), quantity, placement, and dataflow. While recent works have focused on 2D DNN systolic arrays, 2D scaling has been saturating and, thus, improving the performance and power characteristics of computing systems is becoming increasingly challenging. To overcome traditional scaling bottlenecks, 3D integration has emerged as a promising integration technology. 3D technology provides several benefits over 2D systems such as high integration density, high bandwidth, high energy efficiency, and footprint savings. This thesis focuses on two 3D integration technologies: (i) die-stacked 3D (TSV3D), and (ii) monolithic 3D (MONO3D). Both of these 3D technologies provide significant performance and power benefits over 2D systems and thus, are potent technologies for energy efficient design of systolic arrays for DNNs. However, the dense integration in 3D causes high power densities and inter-tier thermal coupling, further escalating thermal issues and resulting in hot spots across tiers. Furthermore, mobile/edge devices have tight area, power, and thermal constraints due to the absence of heat sinks and fans. Thus, temperature is a critical design concern in 3D DNN accelerators for mobile/edge devices. This thesis states that to glean the benefits of 3D technology in mobile/edge devices to improve energy efficiency and satisfy performance and power constraints, it is imperative to design thermally-aware 3D systolic arrays for DNNs. To realize this statement, this thesis makes the following contributions: (i) it designs a thermally-aware optimization flow to select a near-optimal MONO3D DNN systolic array for a given DNN and an optimization goal under a performance constraint. The optimizer is facilitated by circuit and architecture-level cross-layer performance/power models that are developed as part of this thesis. (ii) It introduces thermal awareness in tuning a given TSV3D systolic array chiplet architecture and the chiplet’s placement in a multi-chip module (MCM) executing a multi-DNN workload to balance both cost and power of the MCM, while satisfying latency, area, power, thermal packaging, and workload constraints. (iii) It optimizes a dataflow implementation by utilizing the massive bandwidth available in MONO3D systolic arrays with a dense on-chip resistive RAM to improve energy efficiency while satisfying the thermal and performance constraints. Results demonstrate 81% improvement in inference per second per watt over 2D systolic arrays due to high-density and high-bandwidth resistive RAM interface using monolithic inter-tier vias (MIVs). We also demonstrate up to 44% MCM cost savings and 63% DRAM power savings over temperature-unaware optimization at iso-frequency and iso-MCM area for TSV3D MCMs. In addition, we show that optimization without thermal awareness leads to over-estimation of efficiency gains and thermal violations in both MONO3D and TSV3D systolic arrays. / 2025-01-16T00:00:00Z