Design and modelling of variability tolerant on-chip communication structures for future high performance system on chip designs

The incessant technology scaling has enabled the integration of functionally complex System-on-Chip (SoC) designs with a large number of heterogeneous systems on a single chip. The processing elements on these chips are integrated through on-chip communication structures which provide the infrastructure necessary for the exchange of data and control signals, while meeting the strenuous physical and design constraints. The use of vast amounts of on chip communications will be central to future designs where variability is an inherent characteristic. For this reason, in this thesis we investigate the performance and variability tolerance of typical on-chip communication structures. Understanding of the relationship between variability and communication is paramount for the designers; i.e. to devise new methods and techniques for designing performance and power efficient communication circuits in the forefront of challenges presented by deep sub-micron (DSM) technologies. The initial part of this work investigates the impact of device variability due to Random Dopant Fluctuations (RDF) on the timing characteristics of basic communication elements. The characterization data so obtained can be used to estimate the performance and failure probability of simple links through the methodology proposed in this work. For the Statistical Static Timing Analysis (SSTA) of larger circuits, a method for accurate estimation of the probability density functions of different circuit parameters is proposed. Moreover, its significance on pipelined circuits is highlighted. Power and area are one of the most important design metrics for any integrated circuit (IC) design. This thesis emphasises the consideration of communication reliability while optimizing for power and area. A methodology has been proposed for the simultaneous optimization of performance, area, power and delay variability for a repeater inserted interconnect. Similarly for multi-bit parallel links, bandwidth driven optimizations have also been performed. Power and area efficient semi-serial links, less vulnerable to delay variations than the corresponding fully parallel links are introduced. Furthermore, due to technology scaling, the coupling noise between the link lines has become an important issue. With ever decreasing supply voltages, and the corresponding reduction in noise margins, severe challenges are introduced for performing timing verification in the presence of variability. For this reason an accurate model for crosstalk noise in an interconnection as a function of time and skew is introduced in this work. This model can be used for the identification of skew condition that gives maximum delay noise, and also for efficient design verification

Circuit design and analysis for on-FPGA communication systems

On-chip communication system has emerged as a prominently important subject in Very-Large- Scale-Integration (VLSI) design, as the trend of technology scaling favours logics more than interconnects. Interconnects often dictates the system performance, and, therefore, research for new methodologies and system architectures that deliver high-performance communication services across the chip is mandatory. The interconnect challenge is exacerbated in Field-Programmable Gate Array (FPGA), as a type of ASIC where the hardware can be programmed post-fabrication. Communication across an FPGA will be deteriorating as a result of interconnect scaling. The programmable fabrics, switches and the specific routing architecture also introduce additional latency and bandwidth degradation further hindering intra-chip communication performance. Past research efforts mainly focused on optimizing logic elements and functional units in FPGAs. Communication with programmable interconnect received little attention and is inadequately understood. This thesis is among the first to research on-chip communication systems that are built on top of programmable fabrics and proposes methodologies to maximize the interconnect throughput performance. There are three major contributions in this thesis: (i) an analysis of on-chip interconnect fringing, which degrades the bandwidth of communication channels due to routing congestions in reconfigurable architectures; (ii) a new analogue wave signalling scheme that significantly improves the interconnect throughput by exploiting the fundamental electrical characteristics of the reconfigurable interconnect structures. This new scheme can potentially mitigate the interconnect scaling challenges. (iii) a novel Dynamic Programming (DP)-network to provide adaptive routing in network-on-chip (NoC) systems. The DP-network architecture performs runtime optimization for route planning and dynamic routing which, effectively utilizes the in-silicon bandwidth. This thesis explores a new horizon in reconfigurable system design, in which new methodologies and concepts are proposed to enhance the on-FPGA communication throughput performance that is of vital importance in new technology processes

Design and application of reconfigurable circuits and systems

Open Acces

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective

On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential. While algorithmic advances in neuromorphic computing are proceeding successfully, the potential of memristors to improve neuromorphic computing have not yet born fruit, primarily because they are often used as a drop-in replacement to conventional memory. However, interdisciplinary approaches anchored in machine learning theory suggest that multifactor plasticity rules matching neural and synaptic dynamics to the device capabilities can take better advantage of memristor dynamics and its stochasticity. Furthermore, such plasticity rules generally show much higher performance than that of classical Spike Time Dependent Plasticity (STDP) rules. This chapter reviews the recent development in learning with spiking neural network models and their possible implementation with memristor-based hardware

Doctor of Philosophy

dissertationCommunication surpasses computation as the power and performance bottleneck in forthcoming exascale processors. Scaling has made transistors cheap, but on-chip wires have grown more expensive, both in terms of latency as well as energy. Therefore, the need for low energy, high performance interconnects is highly pronounced, especially for long distance communication. In this work, we examine two aspects of the global signaling problem. The first part of the thesis focuses on a high bandwidth asynchronous signaling protocol for long distance communication. Asynchrony among intellectual property (IP) cores on a chip has become necessary in a System on Chip (SoC) environment. Traditional asynchronous handshaking protocol suffers from loss of throughput due to the added latency of sending the acknowledge signal back to the sender. We demonstrate a method that supports end-to-end communication across links with arbitrarily large latency, without limiting the bandwidth, so long as line variation can be reliably controlled. We also evaluate the energy and latency improvements as a result of the design choices made available by this protocol. The use of transmission lines as a physical interconnect medium shows promise for deep submicron technologies. In our evaluations, we notice a lower energy footprint, as well as vastly reduced wire latency for transmission line interconnects. We approach this problem from two sides. Using field solvers, we investigate the physical design choices to determine the optimal way to implement these lines for a given back-end-of-line (BEOL) stack. We also approach the problem from a system designer's viewpoint, looking at ways to optimize the lines for different performance targets. This work analyzes the advantages and pitfalls of implementing asynchronous channel protocols for communication over long distances. Finally, the innovations resulting from this work are applied to a network-on-chip design example and the resulting power-performance benefits are reported

Intrinsically Evolvable Artificial Neural Networks

Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented

Accelerating RSA Public Key Cryptography via Hardware Acceleration

A large number and a variety of sensors and actuators, also known as edge devices of the Internet of Things, belonging to various industries - health care monitoring, home automation, industrial automation, have become prevalent in today\u27s world. These edge devices need to communicate data collected to the central system occasionally and often in burst mode which is then used for monitoring and control purposes. To ensure secure connections, Asymmetric or Public Key Cryptography (PKC) schemes are used in combination with Symmetric Cryptography schemes. RSA (Rivest - Shamir- Adleman) is one of the most prevalent public key cryptosystems, and has computationally intensive operations which might have a high latency when implemented in resource constrained environments. The objective of this thesis is to design an accelerator capable of increasing the speed of execution of the RSA algorithm in such resource constrained environments. The bottleneck of the algorithm is determined by analyzing the performance of the algorithm in various platforms - Intel Linux Machine, Raspberry Pi, Nios soft core processor. In designing the accelerator to speedup bottleneck function, we realize that the accelerator architecture will need to be changed according to the resources available to the accelerator. We use high level synthesis tools to explore the design space of the accelerator by taking into consideration system level aspects like the number of ports available to transfer inputs to the accelerator, the word size of the processor, etc. We also propose a new accelerator architecture for the bottleneck function and the algorithm it implements and compare the area and latency requirements of it with other designs obtained from design space exploration. The functionality of the design proposed is verified and prototyped in Zynq SoC of Xilinx Zedboard