Autonomous Recovery Of Reconfigurable Logic Devices Using Priority Escalation Of Slack

Field Programmable Gate Array (FPGA) devices offer a suitable platform for survivable hardware architectures in mission-critical systems. In this dissertation, active dynamic redundancy-based fault-handling techniques are proposed which exploit the dynamic partial reconfiguration capability of SRAM-based FPGAs. Self-adaptation is realized by employing reconfiguration in detection, diagnosis, and recovery phases. To extend these concepts to semiconductor aging and process variation in the deep submicron era, resilient adaptable processing systems are sought to maintain quality and throughput requirements despite the vulnerabilities of the underlying computational devices. A new approach to autonomous fault-handling which addresses these goals is developed using only a uniplex hardware arrangement. It operates by observing a health metric to achieve Fault Demotion using Recon- figurable Slack (FaDReS). Here an autonomous fault isolation scheme is employed which neither requires test vectors nor suspends the computational throughput, but instead observes the value of a health metric based on runtime input. The deterministic flow of the fault isolation scheme guarantees success in a bounded number of reconfigurations of the FPGA fabric. FaDReS is then extended to the Priority Using Resource Escalation (PURE) online redundancy scheme which considers fault-isolation latency and throughput trade-offs under a dynamic spare arrangement. While deep-submicron designs introduce new challenges, use of adaptive techniques are seen to provide several promising avenues for improving resilience. The scheme developed is demonstrated by hardware design of various signal processing circuits and their implementation on a Xilinx Virtex-4 FPGA device. These include a Discrete Cosine Transform (DCT) core, Motion Estimation (ME) engine, Finite Impulse Response (FIR) Filter, Support Vector Machine (SVM), and Advanced Encryption Standard (AES) blocks in addition to MCNC benchmark circuits. A iii significant reduction in power consumption is achieved ranging from 83% for low motion-activity scenes to 12.5% for high motion activity video scenes in a novel ME engine configuration. For a typical benchmark video sequence, PURE is shown to maintain a PSNR baseline near 32dB. The diagnosability, reconfiguration latency, and resource overhead of each approach is analyzed. Compared to previous alternatives, PURE maintains a PSNR within a difference of 4.02dB to 6.67dB from the fault-free baseline by escalating healthy resources to higher-priority signal processing functions. The results indicate the benefits of priority-aware resiliency over conventional redundancy approaches in terms of fault-recovery, power consumption, and resource-area requirements. Together, these provide a broad range of strategies to achieve autonomous recovery of reconfigurable logic devices under a variety of constraints, operating conditions, and optimization criteria

Accurate Settling-Time Modeling and Design Procedures for Two-Stage Miller-Compensated Amplifiers for Switched-Capacitor Circuits

We present modeling techniques for accurate estimation of settling errors in switched-capacitor (SC) circuits built with Miller-compensated operational transconductance amplifiers (OTAs). One distinctive feature of the proposal is the computation of the impact of signal levels (on both the model parameters and the model structure) as they change during transient evolution. This is achieved by using an event-driven behavioral approach that combines small- and large-signal behavioral descriptions and keeps track of the amplifier state after each clock phase. Also, SC circuits are modeled under closed-loop conditions to guarantee that the results remain close to those obtained by electrical simulation of the actual circuits. Based on these models, which can be regarded as intermediate between the more established small-signal approach and full-fledged simulations, design procedures for dimensioning SC building blocks are presented whose targets are system-level specifications (such as ENOB and SNDR) instead of OTA specifications. The proposed techniques allow to complete top-down model-based designs with 0.3-b accuracy.Ministerio de Educación y Ciencia TEC2006-03022Junta de Andalucía TIC-0281

Custom Cell Placement Automation for Asynchronous VLSI

Asynchronous Very-Large-Scale-Integration (VLSI) integrated circuits have demonstrated many advantages over their synchronous counterparts, including low power consumption, elastic pipelining, robustness against manufacturing and temperature variations, etc. However, the lack of dedicated electronic design automation (EDA) tools, especially physical layout automation tools, largely limits the adoption of asynchronous circuits. Existing commercial placement tools are optimized for synchronous circuits, and require a standard cell library provided by semiconductor foundries to complete the physical design. The physical layouts of cells in this library have the same height to simplify the placement problem and the power distribution network. Although the standard cell methodology also works for asynchronous designs, the performance is inferior compared with counterparts designed using the full-custom design methodology. To tackle this challenge, we propose a gridded cell layout methodology for asynchronous circuits, in which the cell height and cell width can be any integer multiple of two grid values. The gridded cell approach combines the shape regularity of standard cells with the size flexibility of full-custom layouts. Therefore, this approach can achieve a better space utilization ratio and lower wire length for asynchronous designs. Experiments have shown that the gridded cell placement approach reduces area without impacting the routability. We have also used this placer to tape out a chip in a 65nm process technology, demonstrating that our placer generates design-rule clean results

Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.Comment: (Under review

An effective AMS Top-Down Methodology Applied to the Design of a Mixed-SignalUWB System-on-Chip

The design of Ultra Wideband (UWB) mixed-signal SoC for localization applications in wireless personal area networks is currently investigated by several researchers. The complexity of the design claims for effective top-down methodologies. We propose a layered approach based on VHDL-AMS for the first design stages and on an intelligent use of a circuit-level simulator for the transistor-level phase. We apply the latter just to one block at a time and wrap it within the system-level VHDL-AMS description. This method allows to capture the impact of circuit-level design choices and non-idealities on system performance. To demonstrate the effectiveness of the methodology we show how the refinement of the design affects specific UWB system parameters such as bit-error rate and localization estimations

Simulation of intrinsic parameter fluctuations in decananometer and nanometer-scale MOSFETs

Intrinsic parameter fluctuations introduced by discreteness of charge and matter will play an increasingly important role when semiconductor devices are scaled to decananometer and nanometer dimensions in next-generation integrated circuits and systems. In this paper, we review the analytical and the numerical simulation techniques used to study and predict such intrinsic parameters fluctuations. We consider random discrete dopants, trapped charges, atomic-scale interface roughness, and line edge roughness as sources of intrinsic parameter fluctuations. The presented theoretical approach based on Green's functions is restricted to the case of random discrete charges. The numerical simulation approaches based on the drift diffusion approximation with density gradient quantum corrections covers all of the listed sources of fluctuations. The results show that the intrinsic fluctuations in conventional MOSFETs, and later in double gate architectures, will reach levels that will affect the yield and the functionality of the next generation analog and digital circuits unless appropriate changes to the design are made. The future challenges that have to be addressed in order to improve the accuracy and the predictive power of the intrinsic fluctuation simulations are also discussed

Advanced Wireless Digital Baseband Signal Processing Beyond 100 Gbit/s

International audienceThe continuing trend towards higher data rates in wireless communication systems will, in addition to a higher spectral efficiency and lowest signal processing latencies, lead to throughput requirements for the digital baseband signal processing beyond 100 Gbit/s, which is at least one order of magnitude higher than the tens of Gbit/s targeted in the 5G standardization. At the same time, advances in silicon technology due to shrinking feature sizes and increased performance parameters alone won't provide the necessary gain, especially in energy efficiency for wireless transceivers, which have tightly constrained power and energy budgets. In this paper, we highlight the challenges for wireless digital baseband signal processing beyond 100 Gbit/s and the limitations of today's architectures. Our focus lies on the channel decoding and MIMO detection, which are major sources of complexity in digital baseband signal processing. We discuss techniques on algorithmic and architectural level, which aim to close this gap. For the first time we show Turbo-Code decoding techniques towards 100 Gbit/s and a complete MIMO receiver beyond 100 Gbit/s in 28 nm technology