Functional testing of faults in asynchronous crossbar architecture

The challenge of extending Moore\u27s Law past the physical limits of the present semiconductor technology calls for novel innovations. Several novel nanotechnologies are being proposed as an alternative to their CMOS counterparts, with nanowire crossbar being one of the most promising paradigms. Quite recently, a new promising clock-free architecture, called the Asynchronous Crossbar Architecture has been proposed to enhance the manufacturability and to improve the robustness of digital circuits by removing various timing related failure modes. Even though the proposed clock-free architecture offers several merits, it is not free from the high defect rates induced due to nondeterministic nanoscale assembly. In this work, a unique Functional Test Algorithm (FTA) has been proposed and validated to test for manufacturing defects in this architecture. The proposed Functional Test Algorithm is aimed at reducing the testing overhead in terms of the time and space complexity associated with the existing sequential test scheme. In addition, it is designed to provide high fault coverage and excellent fault-tolerance via post-reconfiguration. This test scheme can be effectively used to assure true functionality of any threshold gate realized on a given PGMB. The main motivation behind this research is to propose a comprehensive test scheme which can achieve sufficiently high test coverage with acceptable test overhead. This test algorithm is a significant effort towards viable nanoscale computation --Abstract, page iv

Physical principles of memory and logic devices based on nanostructured Dirac materials

During the las decades, the silicon-based semiconductor industry has enabled higher performance per cost of integrated circuits due to the ability of nearly doubling the amount of transistors per chip every two years, however, this has resulted in overheating issues and fundamental manufacturing problems that are very di¿cult to solve. Therefore, Dirac materials (DMs), such as graphene and topological insulators (TIs), are being extensively investigated as possible candidates for replacing silicon-channel devices in the next-generation integrated circuits, due to their attractive ultrahigh carrier mobility and possibility of quantum e¿ects that may be useful for electronic applications. This requires to study the physical principles of such nanostructures to e¿ectivelypredictthequantumtransportbehaviorofpossibledevices. Theaimofthis work is to explore the physical properties of Dirac material-based nanostructures that could be used for novel memory and logic devices, by using tight-binding (TB) and density function theory (DFT) methods combined with the non-equilibrium function (NEGF) formulationDoctoradoDOCTOR(A) EN INGENIERÍA ELECTRICA Y ELECTRÓNIC

Cross-layer reliability evaluation, moving from the hardware architecture to the system level: A CLERECO EU project overview

Advanced computing systems realized in forthcoming technologies hold the promise of a significant increase of computational capabilities. However, the same path that is leading technologies toward these remarkable achievements is also making electronic devices increasingly unreliable. Developing new methods to evaluate the reliability of these systems in an early design stage has the potential to save costs, produce optimized designs and have a positive impact on the product time-to-market. CLERECO European FP7 research project addresses early reliability evaluation with a cross-layer approach across different computing disciplines, across computing system layers and across computing market segments. The fundamental objective of the project is to investigate in depth a methodology to assess system reliability early in the design cycle of the future systems of the emerging computing continuum. This paper presents a general overview of the CLERECO project focusing on the main tools and models that are being developed that could be of interest for the research community and engineering practice

Hardware Architectures and Implementations for Associative Memories : the Building Blocks of Hierarchically Distributed Memories

During the past several decades, the semiconductor industry has grown into a global industry with revenues around $300 billion. Intel no longer relies on only transistor scaling for higher CPU performance, but instead, focuses more on multiple cores on a single die. It has been projected that in 2016 most CMOS circuits will be manufactured with 22 nm process. The CMOS circuits will have a large number of defects. Especially when the transistor goes below sub-micron, the original deterministic circuits will start having probabilistic characteristics. Hence, it would be challenging to map traditional computational models onto probabilistic circuits, suggesting a need for fault-tolerant computational algorithms. Biologically inspired algorithms, or associative memories (AMs)—the building blocks of cortical hierarchically distributed memories (HDMs) discussed in this dissertation, exhibit a remarkable match to the nano-scale electronics, besides having great fault-tolerance ability. Research on the potential mapping of the HDM onto CMOL (hybrid CMOS/nanoelectronic circuits) nanogrids provides useful insight into the development of non-von Neumann neuromorphic architectures and semiconductor industry. In this dissertation, we investigated the implementations of AMs on different hardware platforms, including microprocessor based personal computer (PC), PC cluster, field programmable gate arrays (FPGA), CMOS, and CMOL nanogrids. We studied two types of neural associative memory models, with and without temporal information. In this research, we first decomposed the computational models into basic and common operations, such as matrix-vector inner-product and k-winners-take-all (k-WTA). We then analyzed the baseline performance/price ratio of implementing the AMs with a PC. We continued with a similar performance/price analysis of the implementations on more parallel hardware platforms, such as PC cluster and FPGA. However, the majority of the research emphasized on the implementations with all digital and mixed-signal full-custom CMOS and CMOL nanogrids. In this dissertation, we draw the conclusion that the mixed-signal CMOL nanogrids exhibit the best performance/price ratio over other hardware platforms. We also highlighted some of the trade-offs between dedicated and virtualized hardware circuits for the HDM models. A simple time-multiplexing scheme for the digital CMOS implementations can achieve comparable throughput as the mixed-signal CMOL nanogrids