Implementation of arithmetic primitives using truly deep submicron technology (TDST)

The invention of the transistor in 1947 at Bell Laboratories revolutionised the electronics industry and created a powerful platform for emergence of new industries. The quest to increase the number of devices per chip over the last four decades has resulted in rapid transition from Small-Scale-Integration (SSI) and Large-Scale-lntegration (LSI), through to the Very-Large-Scale-Integration (VLSI) technologies, incorporating approximately 10 to 100 million devices per chip. The next phase in this evolution is the Ultra-Large-Scale-Integration (ULSI) aiming to realise new application domains currently not accessible to CMOS technology. Although technology is continuously evolving to produce smaller systems with minimised power dissipation, the IC industry is facing major challenges due to constraints on power density (W/cm2) and high dynamic (operating) and static (standby) power dissipation. Mobile multimedia communication and optical based technologies have rapidly become a significant area of research and development challenging a variety of technological fronts. The future emergence or 4G (4th Generation) wireless communications networks is further driving this development, requiring increasing levels of media rich content. The processing requirements for capture, conversion, compression, decompression, enhancement and display of higher quality multimedia, place heavy demands on current ULSI systems. This is also apparent for mobile applications and intelligent optical networks where silicon chip area and power dissipation become primary considerations. In addition to the requirements for very low power, compact size and real-time processing, the rapidly evolving nature of telecommunication networks means that flexible soft programmable systems capable of adaptation to support a number of different standards and/or roles become highly desirable. In order to fully realise the capabilities promised by the 4G and supporting intelligent networks, new enabling technologies arc needed to facilitate the next generation of personal communications devices. Most of the current solutions to meet these challenges are based on various implementations of conventional architectures. For decades, silicon has been the main platform of computing, however it is slow, bulky, runs too hot, and is too expensive. Thus, new approaches to architectures, driving multimedia and future telecommunications systems, are needed in order to extend the life cycle of silicon technology. The emergence of Truly Deep Submicron Technology (TDST) and related 3-D interconnection technologies have provided potential alternatives from conventional architectures to 3-D system solutions, through integration of IDST, Vertical Software Mapping and Intelligent Interconnect Technology (IIT). The concept of Soft-Chip Technology (SCT) entails integration of Soft• Processing Circuits with Soft-Configurable Circuits . This concept can effectively manipulate hardware primitives through vertical integration of control and data. Thus the notion of 3-D Soft-Chip emerges as a new design algorithm for content-rich multimedia, telecommunication and intelligent networking system applications. 3•D architectures (design algorithms used suitable for 3-D soft-chip technology), are driven by three factors. The first is development of new device technology (TDST) that can support new architectures with complexities of 100M to 1000M devices. The second is development of advanced wafer bonding techniques such as Indium bump and the more futuristic optical interconnects for 3-D soft-chip mapping. The third is related to improving the performance of silicon CMOS systems as devices continue to scale down in dimensions. One of the fundamental building blocks of any computer system is the arithmetic component. Optimum performance of the system is determined by the efficiency of each individual component, as well as the network as a whole entity. Development of configurable arithmetic primitives is the fundamental focus in 3-D architecture design where functionality can be implemented through soft configurable hardware elements. Therefore the ability to improve the performance capability of a system is of crucial importance for a successful design. Important factors that predict the efficiency of such arithmetic components are: • The propagation delay of the circuit, caused by the gate, diffusion and wire capacitances within !he circuit, minimised through transistor sizing. and • Power dissipation, which is generally based on node transition activity. [2] Although optimum performance of 3-D soft-chip systems is primarily established by the choice of basic primitives such as adders and multipliers, the interconnecting network also has significant degree of influence on !he efficiency of the system. 3-D superposition of devices can decrease interconnect delays by up to 60% compared to a similar planar architecture. This research is based on development and implementation of configurable arithmetic primitives, suitable to the 3-D architecture, and has these foci: • To develop a variety of arithmetic components such as adders and multipliers with particular emphasis on minimum area and compatible with 3-D soft-chip design paradigm. • To explore implementation of configurable distributed primitives for arithmetic processing. This entails optimisation of basic primitives, and using them as part of array processing. In this research the detailed designs of configurable arithmetic primitives are implemented using TDST O.l3µm (130nm) technology, utilising CAD software such as Mentor Graphics and Cadence in Custom design mode, carrying through design, simulation and verification steps

Synthèse de réseaux de distribution d'horloges en présence de variations du procédé de fabrication

Design of clock distributions networks in presence of process variations -- Importance des variations spatiales de la constante de temps du transistor MOS -- Pipelined H-trees for high-speed clocking of large integrated systems in presence of process variations -- Conception de réseaux de distribution d'horloges fiables et à faible consommation de puissance -- Design of low-power and reliable logic-based H-trees -- Sources des variations spatiales de la constante de temps du transistor MOS -- Spatial characterization of process variations via MOS transistor time constants in VLSI & WSI -- Techniques de minimisation du biais de synchronisation par calibration de délai -- Minimizing process-induced skew using delay tuning

Circuit paradigm in the 21

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An instruction systolic array architecture for multiple neural network types

Modern electronic systems, especially sensor and imaging systems, are beginning to incorporate their own neural network subsystems. In order for these neural systems to learn in real-time they must be implemented using VLSI technology, with as much of the learning processes incorporated on-chip as is possible. The majority of current VLSI implementations literally implement a series of neural processing cells, which can be connected together in an arbitrary fashion. Many do not perform the entire neural learning process on-chip, instead relying on other external systems to carry out part of the computation requirements of the algorithm. The work presented here utilises two dimensional instruction systolic arrays in an attempt to define a general neural architecture which is closer to the biological basis of neural networks - it is the synapses themselves, rather than the neurons, that have dedicated processing units. A unified architecture is described which can be programmed at the microcode level in order to facilitate the processing of multiple neural network types. An essential part of neural network processing is the neuron activation function, which can range from a sequential algorithm to a discrete mathematical expression. The architecture presented can easily carry out the sequential functions, and introduces a fast method of mathematical approximation for the more complex functions. This can be evaluated on-chip, thus implementing the entire neural process within a single system. VHDL circuit descriptions for the chip have been generated, and the systolic processing algorithms and associated microcode instruction set for three different neural paradigms have been designed. A software simulator of the architecture has been written, giving results for several common applications in the field

Aspects of parallel processing and control engineering

The concept of parallel processing is not a new one, but the application of it to control engineering tasks is a relatively recent development, made possible by contemporary hardware and software innovation. It has long been accepted that, if properly orchestrated several processors/CPUs when combined can form a powerful processing entity. What prevented this from being implemented in commercial systems was the adequacy of the microprocessor for most tasks and hence the expense of a multi-processor system was not justified. With the advent of high demand systems, such as highly fault tolerant flight controllers and fast robotic controllers, parallel processing became a viable option. Nonetheless, the software interfacing of control laws onto parallel systems has remained somewhat of an impasse. There are no software compilers at present which allow a programmer to specify a control law in pure mathematical terminology and then decompose it into a flow diagram of concurrent processes which may then be implemented on, say, a target Transputer system, liiere are several parallel programming languages with which a programmer can generate parallel processes but, generally, in order to realise a control algorithm in parallel the programmer must have intimate knowledge of the algorithm. Therefore, efficiency is based on the ability of the programmer to recognise inherent parellelism. Some attempts are being made to create intelligent partition and scheduling compilers but this usually means significantly extra overheads on the multiprocessor system. In the absence of an automated technique control algorithms must be decomposed by inspection. The research presented in this thesis is founded upon the application of both parallel and pipelining techniques to particular control strategies. Parallelism is tackled objectively and by creating a tailored terminology it is defined mathematically, and consequently related concepts, such as bounded parallelism and algorithm speedup, are also quantified in a numerical sense. A pipelined explicit Self Tuning Regulator (STR) controller is developed and tested on systems of different order. Under the governance of the parallelism terminology the effectiveness of the parallel STR is evaluated and numerically quantified in terms of relevant performance indices. A parallel simulator is presented for the Puma 560 robotic manipulator. By exploiting parallelism and pipelinability in the robot model a significant increase in execution speed is achieved over the sequential model. The use of Transputers is examined and graphical results obtained for several performance indices, including speedup, processor efficiency and bounded parallelism. By the same analytical technique a parallel computed torque feedforward controller incorporating proportional derivative feedback control for the Puma 560 manipulator is developed and appraised. The performance of a Transputer system in hosting the controller is graphically analysed and as in the case of the parallel simulator the more important performance indices are examined under both optimal conditions and conditions of varying hardware constraints