Design of microprocessor-based hardware for number theoretic transform implementation

Number Theoretic Transforms (NTTs) are defined in a finite ring of integers Z (_M), where M is the modulus. All the arithmetic operations are carried out modulo M. NTTs are similar in structure to DFTs, hence fast FFT type algorithms may be used to compute NTTs efficiently. A major advantage of the NTT is that it can be used to compute error free convolutions, unlike the FFT it is not subject to round off and truncation errors. In 1976 Winograd proposed a set of short length DFT algorithms using a fewer number of multiplications and approximately the same number of additions as the Cooley-Tukey FFT algorithm. This saving is accomplished at the expense of increased algorithm complexity. These short length DFT algorithms may be combined to perform longer transforms. The Winograd Fourier Transform Algorithm (WFTA) was implemented on a TMS9900 microprocessor to compute NTTs. Since multiplication conducted modulo M is very time consuming a special purpose external hardware modular multiplier was designed, constructed and interfaced with the TMS9900 microprocessor. This external hardware modular multiplier allowed an improvement in the transform execution time. Computation time may further be reduced by employing several microprocessors. Taking advantage of the inherent parallelism of the WFTA, a dedicated parallel microprocessor system was designed and constructed to implement a 15-point WFTA in parallel. Benchmark programs were written to choose a suitable microprocessor for the parallel microprocessor system. A master or a host microprocessor is used to control the parallel microprocessor system and provides an interface to the outside world. An analogue to digital (A/D) and a digital to analogue (D/A) converter allows real time digital signal processing

Number theoretic techniques applied to algorithms and architectures for digital signal processing

Many of the techniques for the computation of a two-dimensional convolution of a small fixed window with a picture are reviewed. It is demonstrated that Winograd's cyclic convolution and Fourier Transform Algorithms, together with Nussbaumer's two-dimensional cyclic convolution algorithms, have a common general form. Many of these algorithms use the theoretical minimum number of general multiplications. A novel implementation of these algorithms is proposed which is based upon one-bit systolic arrays. These systolic arrays are networks of identical cells with each cell sharing a common control and timing function. Each cell is only connected to its nearest neighbours. These are all attractive features for implementation using Very Large Scale Integration (VLSI). The throughput rate is only limited by the time to perform a one-bit full addition. In order to assess the usefulness to these systolic arrays a 'cost function' is developed to compare them with more conventional techniques, such as the Cooley-Tukey radix-2 Fast Fourier Transform (FFT). The cost function shows that these systolic arrays offer a good way of implementing the Discrete Fourier Transform for transforms up to about 30 points in length. The cost function is a general tool and allows comparisons to be made between different implementations of the same algorithm and between dissimilar algorithms. Finally a technique is developed for the derivation of Discrete Cosine Transform (DCT) algorithms from the Winograd Fourier Transform Algorithm. These DCT algorithms may be implemented by modified versions of the systolic arrays proposed earlier, but requiring half the number of cells

Digital Signal Processing

Contains reports on twelve research projects.U. S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ENG76-24117)National Aeronautics and Space Administration (Grant NSG-5157)Joint Services Electronics Program (Contract DAABO7-76-C-1400)U.S. Navy-Office of Naval Research (Contract N00014-77-C-0196)Woods Hole Oceanographic InstitutionU. S. Navy - Office of Naval Research (Contract N00014-75-C-0852)Department of Ocean Engineering, M.I.T.National Science Foundation subcontract to Grant GX 41962 to Woods Hole Oceanographic Institutio

The inherent overlapping in the parallel calculation of the Laplacian

Producción CientíficaA new approach for the parallel computation of the Laplacian in the Fourier domain is presented. This numerical problem inherits the intrinsic sequencing involved in the calculation of any multidimensional Fast Fourier Transform (FFT) where blocking communications assure that its computation is strictly carried out dimension by dimension. Such data dependency vanishes when one considers the Laplacian as the sum of n independent one-dimensional kernels, so that computation and communication can be naturally overlapped with nonblocking communications. Overlapping is demonstrated to be responsible for the speedup figures we obtain when our approach is compared to state-of-the-art parallel multidimensional FFTs.Junta de Castilla León (grant number VA296P18

DFT algorithms for bit-serial GaAs array processor architectures

Systems and Processes Engineering Corporation (SPEC) has developed an innovative array processor architecture for computing Fourier transforms and other commonly used signal processing algorithms. This architecture is designed to extract the highest possible array performance from state-of-the-art GaAs technology. SPEC's architectural design includes a high performance RISC processor implemented in GaAs, along with a Floating Point Coprocessor and a unique Array Communications Coprocessor, also implemented in GaAs technology. Together, these data processors represent the latest in technology, both from an architectural and implementation viewpoint. SPEC has examined numerous algorithms and parallel processing architectures to determine the optimum array processor architecture. SPEC has developed an array processor architecture with integral communications ability to provide maximum node connectivity. The Array Communications Coprocessor embeds communications operations directly in the core of the processor architecture. A Floating Point Coprocessor architecture has been defined that utilizes Bit-Serial arithmetic units, operating at very high frequency, to perform floating point operations. These Bit-Serial devices reduce the device integration level and complexity to a level compatible with state-of-the-art GaAs device technology

Baseband Processing for 5G and Beyond: Algorithms, VLSI Architectures, and Co-design

In recent years the number of connected devices and the demand for high data-rates have been significantly increased. This enormous growth is more pronounced by the introduction of the Internet of things (IoT) in which several devices are interconnected to exchange data for various applications like smart homes and smart cities. Moreover, new applications such as eHealth, autonomous vehicles, and connected ambulances set new demands on the reliability, latency, and data-rate of wireless communication systems, pushing forward technology developments. Massive multiple-input multiple-output (MIMO) is a technology, which is employed in the 5G standard, offering the benefits to fulfill these requirements. In massive MIMO systems, base station (BS) is equipped with a very large number of antennas, serving several users equipments (UEs) simultaneously in the same time and frequency resource. The high spatial multiplexing in massive MIMO systems, improves the data rate, energy and spectral efficiencies as well as the link reliability of wireless communication systems. The link reliability can be further improved by employing channel coding technique. Spatially coupled serially concatenated codes (SC-SCCs) are promising channel coding schemes, which can meet the high-reliability demands of wireless communication systems beyond 5G (B5G). Given the close-to-capacity error correction performance and the potential to implement a high-throughput decoder, this class of code can be a good candidate for wireless systems B5G. In order to achieve the above-mentioned advantages, sophisticated algorithms are required, which impose challenges on the baseband signal processing. In case of massive MIMO systems, the processing is much more computationally intensive and the size of required memory to store channel data is increased significantly compared to conventional MIMO systems, which are due to the large size of the channel state information (CSI) matrix. In addition to the high computational complexity, meeting latency requirements is also crucial. Similarly, the decoding-performance gain of SC-SCCs also do come at the expense of increased implementation complexity. Moreover, selecting the proper choice of design parameters, decoding algorithm, and architecture will be challenging, since spatial coupling provides new degrees of freedom in code design, and therefore the design space becomes huge. The focus of this thesis is to perform co-optimization in different design levels to address the aforementioned challenges/requirements. To this end, we employ system-level characteristics to develop efficient algorithms and architectures for the following functional blocks of digital baseband processing. First, we present a fast Fourier transform (FFT), an inverse FFT (IFFT), and corresponding reordering scheme, which can significantly reduce the latency of orthogonal frequency-division multiplexing (OFDM) demodulation and modulation as well as the size of reordering memory. The corresponding VLSI architectures along with the application specific integrated circuit (ASIC) implementation results in a 28 nm CMOS technology are introduced. In case of a 2048-point FFT/IFFT, the proposed design leads to 42% reduction in the latency and size of reordering memory. Second, we propose a low-complexity massive MIMO detection scheme. The key idea is to exploit channel sparsity to reduce the size of CSI matrix and eventually perform linear detection followed by a non-linear post-processing in angular domain using the compressed CSI matrix. The VLSI architecture for a massive MIMO with 128 BS antennas and 16 UEs along with the synthesis results in a 28 nm technology are presented. As a result, the proposed scheme reduces the complexity and required memory by 35%–73% compared to traditional detectors while it has better detection performance. Finally, we perform a comprehensive design space exploration for the SC-SCCs to investigate the effect of different design parameters on decoding performance, latency, complexity, and hardware cost. Then, we develop different decoding algorithms for the SC-SCCs and discuss the associated decoding performance and complexity. Also, several high-level VLSI architectures along with the corresponding synthesis results in a 12 nm process are presented, and various design tradeoffs are provided for these decoding schemes

Fast Fourier transforms on energy-efficient application-specific processors

Many of the current applications used in battery powered devices are from digital signal processing, telecommunication, and multimedia domains. Traditionally application-specific fixed-function circuits have been used in these designs in form of application-specific integrated circuits (ASIC) to reach the required performance and energy-efficiency. The complexity of these applications has increased over the years, thus the design complexity has increased even faster, which implies increased design time. At the same time, there are more and more standards to be supported, thus using optimised fixed-function implementations for all the functions in all the standards is impractical. The non-recurring engineering costs for integrated circuits have also increased significantly, so manufacturers can only afford fewer chip iterations. Although tailoring the circuit for a specific application provides the best performance and/or energy-efficiency, such approach lacks flexibility. E.g., if an error is found after the manufacturing, an expensive chip iteration is required. In addition, new functionalities cannot be added afterwards to support evolution of standards. Flexibility can be obtained with software based implementation technologies. Unfortunately, general-purpose processors do not provide the energy-efficiency of the fixed-function circuit designs. A useful trade-off between flexibility and performance is implementation based on application-specific processors (ASP) where programmability provides the flexibility and computational resources customised for the given application provide the performance. In this Thesis, application-specific processors are considered by using fast Fourier transform as the representative algorithm. The architectural template used here is transport triggered architecture (TTA) which resembles very long instruction word machines but the operand execution resembles data flow machines rather than traditional operand triggering. The developed TTA processors exploit inherent parallelism of the application. In addition, several characteristics of the application have been identified and those are exploited by developing customised functional units for speeding up the execution. Several customisations are proposed for the data path of the processor but it is also important to match the memory bandwidth to the computation speed. This calls for a memory organisation supporting parallel memory accesses. The proposed optimisations have been used to improve the energy-efficiency of the processor and experiments show that a programmable solution can have energy-efficiency comparable to fixed-function ASIC designs