DESIGN AND IMPLEMENTATION OF LIFTING BASED DAUBECHIES WAVELET TRANSFORMS USING ALGEBRAIC INTEGERS

Over the past few decades, the demand for digital information has increased drastically. This enormous demand poses serious difficulties on the storage and transmission bandwidth of the current technologies. One possible solution to overcome this approach is to compress the amount of information by discarding all the redundancies. In multimedia technology, various lossy compression techniques are used to compress the raw image data to facilitate storage and to fit the transmission bandwidth. In this thesis, we propose a new approach using algebraic integers to reduce the complexity of the Daubechies-4 (D4) and Daubechies-6 (D6) Lifting based Discrete Wavelet Transforms. The resulting architecture is completely integer based, which is free from the round-off error that is caused in floating point calculations. The filter coefficients of the two transforms of Daubechies family are individually converted to integers by multiplying it with value of 2x, where, x is a random value selected at a point where the quantity of losses is negligible. The wavelet coefficients are then quantized using the proposed iterative individual-subband coding algorithm. The proposed coding algorithm is adopted from the well-known Embedded Zerotree Wavelet (EZW) coding. The results obtained from simulation shows that the proposed coding algorithm proves to be much faster than its predecessor, and at the same time, produces good Peak Signal to Noise Ratio (PSNR) at very low bit rates. Finally, the two proposed transform architectures are implemented on Virtex-E Field Programmable Gate Array (FPGA) to test the hardware cost (in terms of multipliers, adders and registers) and throughput rate. From the synthesis results, we see that the proposed algorithm has low hardware cost and a high throughput rate

Low Power Memory/Memristor Devices and Systems

This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

Boosting the hardware-efficiency of cascade support vector machines for embedded classification applications

Support Vector Machines (SVMs) are considered as a state-of-the-art classification algorithm capable of high accuracy rates for a different range of applications. When arranged in a cascade structure, SVMs can efficiently handle problems where the majority of data belongs to one of the two classes, such as image object classification, and hence can provide speedups over monolithic (single) SVM classifiers. However, the SVM classification process is still computationally demanding due to the number of support vectors. Consequently, in this paper we propose a hardware architecture optimized for cascaded SVM processing to boost performance and hardware efficiency, along with a hardware reduction method in order to reduce the overheads from the implementation of additional stages in the cascade, leading to significant resource and power savings. The architecture was evaluated for the application of object detection on 800×600 resolution images on a Spartan 6 Industrial Video Processing FPGA platform achieving over 30 frames-per-second. Moreover, by utilizing the proposed hardware reduction method we were able to reduce the utilization of FPGA custom-logic resources by ∼30%, and simultaneously observed ∼20% peak power reduction compared to a baseline implementation

Low Latency Audio Processing

PhDLatency in the live audio processing chain has become a concern for audio engineers and system designers because significant delays can be perceived and may affect synchronisation of signals, limit interactivity, degrade sound quality and cause acoustic feedback. In recent years, latency problems have become more severe since audio processing has become digitised, high-resolution ADCs and DACs are used, complex processing is performed, and data communication networks are used for audio signal transmission in conjunction with other traffic types. In many live audio applications, latency thresholds are bounded by human perceptions. The applications such as music ensembles and live monitoring require low delay and predictable latency. Current digital audio systems either have difficulties to achieve or have to trade-off latency with other important audio processing functionalities. This thesis investigated the fundamental causes of the latency in a modern digital audio processing system: group delay, buffering delay, and physical propagation delay and their associated system components. By studying the time-critical path of a general audio system, we focus on three main functional blocks that have the significant impact on overall latency; the high-resolution digital filters in sigma-delta based ADC/DAC, the operating system to process low latency audio streams, and the audio networking to transmit audio with flexibility and convergence. In this work, we formed new theory and methods to reduce latency and accurately predict latency for group delay. We proposed new scheduling algorithms for the operating system that is suitable for low latency audio processing. We designed a new system architecture and new protocols to produce deterministic networking components that can contribute the overall timing assurance and predictability of live audio processing. The results are validated by simulations and experimental tests. Also, this bottom-up approach is aligned with the methodology that could solve the timing problem of general cyber-physical systems that require the integration of communication, software and human interactions

Custom optimization algorithms for efficient hardware implementation

The focus is on real-time optimal decision making with application in advanced control systems. These computationally intensive schemes, which involve the repeated solution of (convex) optimization problems within a sampling interval, require more efficient computational methods than currently available for extending their application to highly dynamical systems and setups with resource-constrained embedded computing platforms. A range of techniques are proposed to exploit synergies between digital hardware, numerical analysis and algorithm design. These techniques build on top of parameterisable hardware code generation tools that generate VHDL code describing custom computing architectures for interior-point methods and a range of first-order constrained optimization methods. Since memory limitations are often important in embedded implementations we develop a custom storage scheme for KKT matrices arising in interior-point methods for control, which reduces memory requirements significantly and prevents I/O bandwidth limitations from affecting the performance in our implementations. To take advantage of the trend towards parallel computing architectures and to exploit the special characteristics of our custom architectures we propose several high-level parallel optimal control schemes that can reduce computation time. A novel optimization formulation was devised for reducing the computational effort in solving certain problems independent of the computing platform used. In order to be able to solve optimization problems in fixed-point arithmetic, which is significantly more resource-efficient than floating-point, tailored linear algebra algorithms were developed for solving the linear systems that form the computational bottleneck in many optimization methods. These methods come with guarantees for reliable operation. We also provide finite-precision error analysis for fixed-point implementations of first-order methods that can be used to minimize the use of resources while meeting accuracy specifications. The suggested techniques are demonstrated on several practical examples, including a hardware-in-the-loop setup for optimization-based control of a large airliner.Open Acces

Design and resource management of reconfigurable multiprocessors for data-parallel applications

FPGA (Field-Programmable Gate Array)-based custom reconfigurable computing machines have established themselves as low-cost and low-risk alternatives to ASIC (Application-Specific Integrated Circuit) implementations and general-purpose microprocessors in accelerating a wide range of computation-intensive applications. Most often they are Application Specific Programmable Circuiits (ASPCs), which are developer programmable instead of user programmable. The major disadvantages of ASPCs are minimal programmability, and significant time and energy overheads caused by required hardware reconfiguration when the problem size outnumbers the available reconfigurable resources; these problems are expected to become more serious with increases in the FPGA chip size. On the other hand, dominant high-performance computing systems, such as PC clusters and SMPs (Symmetric Multiprocessors), suffer from high communication latencies and/or scalability problems. This research introduces low-cost, user-programmable and reconfigurable MultiProcessor-on-a-Programmable-Chip (MPoPC) systems for high-performance, low-cost computing. It also proposes a relevant resource management framework that deals with performance, power consumption and energy issues. These semi-customized systems reduce significantly runtime device reconfiguration by employing userprogrammable processing elements that are reusable for different tasks in large, complex applications. For the sake of illustration, two different types of MPoPCs with hardware FPUs (floating-point units) are designed and implemented for credible performance evaluation and modeling: the coarse-grain MIMD (Multiple-Instruction, Multiple-Data) CG-MPoPC machine based on a processor IP (Intellectual Property) core and the mixed-mode (MIMD, SIMD or M-SIMD) variant-grain HERA (HEterogeneous Reconfigurable Architecture) machine. In addition to alleviating the above difficulties, MPoPCs can offer several performance and energy advantages to our data-parallel applications when compared to ASPCs; they are simpler and more scalable, and have less verification time and cost. Various common computation-intensive benchmark algorithms, such as matrix-matrix multiplication (MMM) and LU factorization, are studied and their parallel solutions are shown for the two MPoPCs. The performance is evaluated with large sparse real-world matrices primarily from power engineering. We expect even further performance gains on MPoPCs in the near future by employing ever improving FPGAs. The innovative nature of this work has the potential to guide research in this arising field of high-performance, low-cost reconfigurable computing. The largest advantage of reconfigurable logic lies in its large degree of hardware customization and reconfiguration which allows reusing the resources to match the computation and communication needs of applications. Therefore, a major effort in the presented design methodology for mixed-mode MPoPCs, like HERA, is devoted to effective resource management. A two-phase approach is applied. A mixed-mode weighted Task Flow Graph (w-TFG) is first constructed for any given application, where tasks are classified according to their most appropriate computing mode (e.g., SIMD or MIMD). At compile time, an architecture is customized and synthesized for the TFG using an Integer Linear Programming (ILP) formulation and a parameterized hardware component library. Various run-time scheduling schemes with different performanceenergy objectives are proposed. A system-level energy model for HERA, which is based on low-level implementation data and run-time statistics, is proposed to guide performance-energy trade-off decisions. A parallel power flow analysis technique based on Newton\u27s method is proposed and employed to verify the methodology

Toatie : functional hardware description with dependent types

Describing correct circuits remains a tall order, despite four decades of evolution in Hardware Description Languages (HDLs). Many enticing circuit architectures require recursive structures or complex compile-time computation — two patterns that prove difficult to capture in traditional HDLs. In a signal processing context, the Fast FIR Algorithm (FFA) structure for efficient parallel filtering proves to be naturally recursive, and most Multiple Constant Multiplication (MCM) blocks decompose multiplications into graphs of simple shifts and adds using demanding compile time computation. Generalised versions of both remain mostly in academic folklore. The implementations which do exist are often ad hoc circuit generators, written in software languages. These pose challenges for verification and are resistant to composition. Embedded functional HDLs, that represent circuits as data, allow for these descriptions at the cost of forcing the designer to work at the gate-level. A promising alternative is to use a stand-alone compiler, representing circuits as plain functions, exemplified by the CλaSH HDL. This, however, raises new challenges in capturing a circuit’s staging — which expressions in the single language should be reduced during compile-time elaboration, and which should remain in the circuit’s run-time? To better reflect the physical separation between circuit phases, this work proposes a new functional HDL (representing circuits as functions) with first-class staging constructs. Orthogonal to this, there are also long-standing challenges in the verification of parameterised circuit families. Industry surveys have consistently reported that only a slim minority of FPGA projects reach production without non-trivial bugs. While a healthy growth in the adoption of automatic formal methods is also reported, the majority of testing remains dynamic — presenting difficulties for testing entire circuit families at once. This research offers an alternative verification methodology via the combination of dependent types and automatic synthesis of user-defined data types. Given precise enough types for synthesisable data, this environment can be used to develop circuit families with full functional verification in a correct-by-construction fashion. This approach allows for verification of entire circuit families (not just one concrete member) and side-steps the state-space explosion of model checking methods. Beyond the existing work, this research offers synthesis of combinatorial circuits — not just a software model of their behaviour. This additional step requires careful consideration of staging, erasure & irrelevance, deriving bit representations of user-defined data types, and a new synthesis scheme. This thesis contributes steps towards HDLs with sufficient expressivity for awkward, combinatorial signal processing structures, allowing for a correct-by-construction approach, and a prototype compiler for netlist synthesis.Describing correct circuits remains a tall order, despite four decades of evolution in Hardware Description Languages (HDLs). Many enticing circuit architectures require recursive structures or complex compile-time computation — two patterns that prove difficult to capture in traditional HDLs. In a signal processing context, the Fast FIR Algorithm (FFA) structure for efficient parallel filtering proves to be naturally recursive, and most Multiple Constant Multiplication (MCM) blocks decompose multiplications into graphs of simple shifts and adds using demanding compile time computation. Generalised versions of both remain mostly in academic folklore. The implementations which do exist are often ad hoc circuit generators, written in software languages. These pose challenges for verification and are resistant to composition. Embedded functional HDLs, that represent circuits as data, allow for these descriptions at the cost of forcing the designer to work at the gate-level. A promising alternative is to use a stand-alone compiler, representing circuits as plain functions, exemplified by the CλaSH HDL. This, however, raises new challenges in capturing a circuit’s staging — which expressions in the single language should be reduced during compile-time elaboration, and which should remain in the circuit’s run-time? To better reflect the physical separation between circuit phases, this work proposes a new functional HDL (representing circuits as functions) with first-class staging constructs. Orthogonal to this, there are also long-standing challenges in the verification of parameterised circuit families. Industry surveys have consistently reported that only a slim minority of FPGA projects reach production without non-trivial bugs. While a healthy growth in the adoption of automatic formal methods is also reported, the majority of testing remains dynamic — presenting difficulties for testing entire circuit families at once. This research offers an alternative verification methodology via the combination of dependent types and automatic synthesis of user-defined data types. Given precise enough types for synthesisable data, this environment can be used to develop circuit families with full functional verification in a correct-by-construction fashion. This approach allows for verification of entire circuit families (not just one concrete member) and side-steps the state-space explosion of model checking methods. Beyond the existing work, this research offers synthesis of combinatorial circuits — not just a software model of their behaviour. This additional step requires careful consideration of staging, erasure & irrelevance, deriving bit representations of user-defined data types, and a new synthesis scheme. This thesis contributes steps towards HDLs with sufficient expressivity for awkward, combinatorial signal processing structures, allowing for a correct-by-construction approach, and a prototype compiler for netlist synthesis

Adaptive Baseband Pro cessing and Configurable Hardware for Wireless Communication

The world of information is literally at one’s fingertips, allowing access to previously unimaginable amounts of data, thanks to advances in wireless communication. The growing demand for high speed data has necessitated theuse of wider bandwidths, and wireless technologies such as Multiple-InputMultiple-Output (MIMO) have been adopted to increase spectral efficiency.These advanced communication technologies require sophisticated signal processing, often leading to higher power consumption and reduced battery life.Therefore, increasing energy efficiency of baseband hardware for MIMO signal processing has become extremely vital. High Quality of Service (QoS)requirements invariably lead to a larger number of computations and a higherpower dissipation. However, recognizing the dynamic nature of the wirelesscommunication medium in which only some channel scenarios require complexsignal processing, and that not all situations call for high data rates, allowsthe use of an adaptive channel aware signal processing strategy to provide adesired QoS. Information such as interference conditions, coherence bandwidthand Signal to Noise Ratio (SNR) can be used to reduce algorithmic computations in favorable channels. Hardware circuits which run these algorithmsneed flexibility and easy reconfigurability to switch between multiple designsfor different parameters. These parameters can be used to tune the operations of different components in a receiver based on feedback from the digitalbaseband. This dissertation focuses on the optimization of digital basebandcircuitry of receivers which use feedback to trade power and performance. Aco-optimization approach, where designs are optimized starting from the algorithmic stage through the hardware architectural stage to the final circuitimplementation is adopted to realize energy efficient digital baseband hardwarefor mobile 4G devices. These concepts are also extended to the next generation5G systems where the energy efficiency of the base station is improved.This work includes six papers that examine digital circuits in MIMO wireless receivers. Several key blocks in these receiver include analog circuits thathave residual non-linearities, leading to signal intermodulation and distortion.Paper-I introduces a digital technique to detect such non-linearities and calibrate analog circuits to improve signal quality. The concept of a digital nonlinearity tuning system developed in Paper-I is implemented and demonstratedin hardware. The performance of this implementation is tested with an analogchannel select filter, and results are presented in Paper-II. MIMO systems suchas the ones used in 4G, may employ QR Decomposition (QRD) processors tosimplify the implementation of tree search based signal detectors. However,the small form factor of the mobile device increases spatial correlation, whichis detrimental to signal multiplexing. Consequently, a QRD processor capableof handling high spatial correlation is presented in Paper-III. The algorithm and hardware implementation are optimized for carrier aggregation, which increases requirements on signal processing throughput, leading to higher powerdissipation. Paper-IV presents a method to perform channel-aware processingwith a simple interpolation strategy to adaptively reduce QRD computationcount. Channel properties such as coherence bandwidth and SNR are used toreduce multiplications by 40% to 80%. These concepts are extended to usetime domain correlation properties, and a full QRD processor for 4G systemsfabricated in 28 nm FD-SOI technology is presented in Paper-V. The designis implemented with a configurable architecture and measurements show thatcircuit tuning results in a highly energy efficient processor, requiring 0.2 nJ to1.3 nJ for each QRD. Finally, these adaptive channel-aware signal processingconcepts are examined in the scope of the next generation of communicationsystems. Massive MIMO systems increase spectral efficiency by using a largenumber of antennas at the base station. Consequently, the signal processingat the base station has a high computational count. Paper-VI presents a configurable detection scheme which reduces this complexity by using techniquessuch as selective user detection and interpolation based signal processing. Hardware is optimized for resource sharing, resulting in a highly reconfigurable andenergy efficient uplink signal detector