A self-reconfigurable hardware architecture for mesh arrays using single/double vertical track switches

科研費報告書収録論文(課題番号:14380138・基盤研究(B)(2)・14～16/研究代表者:堀口, 進 死亡(奥様 堀口悦子)/超高速ノンブロック・ネットワーク構成方式に関する研究

Sensing as a Service: An Exploration into the Practical Implementations of DSA

The cognitive radio literature generally assumes that the functions required for non-cooperative secondary DSA are integrated into a single radio system. It need not be so. In this paper, we model cognitive radio functions as a value chain and explore the implications of different forms of organization of this value chain. We initially explore the consequences of separating the sensing function from other cognitive radio functions

Investigation into the wafer-scale integration of fine-grain parallel processing computer systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis investigates the potential of wafer-scale integration (WSI) for the implementation of low-cost fine-grain parallel processing computer systems. As WSI is a relatively new subject, there was little work on which to base investigations. Indeed, most WSI architectures existed only as untried and sometimes vague proposals. Accordingly, the research strategy approached this problem by identifying a representative WSI structure and architecture on which to base investigations. An analysis of architectural proposals identified associative memory to be general purpose parallel processing component used in a wide range of WSI architectures. Furthermore, this analysis provided a set of WSI-level design requirements to evaluate the sustainability of different architectures as research vehicles. The WSI-ASP (WASP) device, which has a large associative memory as its main component is shown to meet these requirements and hence was chosen as the research vehicle. Consequently, this thesis addresses WSI potential through an in-depth investigation into the feasibility of implementing a large associative memory for the WASP device that meets the demanding technological constraints of WSI. Overall, the thesis concludes that WSI offers significant potential for the implementation of low-cost fine-grain parallel processing computer systems. However, due to the dual constraints of thermal management and the area required for the power distribution network, power density is a major design constraint in WSI. Indeed, it is shown that WSI power densities need to be an order of magnitude lower than VLSI power densities. The thesis demonstrates that for associative memories at least, VLSI designs are unsuited to implementation in WSI. Rather, it is shown that WSI circuits must be closely matched to the operational environment to assure suitable power densities. These circuits are significantly larger than their VLSI equivalents. Nonetheless, the thesis demonstrates that by concentrating on the most power intensive circuits, it is possible to achieve acceptable power densities with only a modest increase in area overheads.SER

A Fault Diagnosis Scheme and Its Quality Issue in Reconfigurable Array Architecture

In this paper, we propose an efficient diagnosis scheme to detect and locate the switching network defects/faults in reconfigurable array architecture. This diagnosis scheme performs the test of switching network based on the scan path and fault intersection test methodology to locate the faults occurring in the switching network. After the diagnosis of switching network, the processing element (PE) test can then be initiated through the good switches and links. Errors in testing that cause a good switch, link or PE to be considered as a bad one is called "killing error". The issue of killing error in testing is addressed and the probability of killing error for our diagnosis technique is analyzed and shown to be extremely low. The significance of this approach is the ability to detect and locate the multiple faults in switches, links, and PEs with low testing circuit overhead, and to offer the good test quality in linear diagnosis time.Facultad de Informátic

Autonomously Reconfigurable Artificial Neural Network on a Chip

Artificial neural network (ANN), an established bio-inspired computing paradigm, has proved very effective in a variety of real-world problems and particularly useful for various emerging biomedical applications using specialized ANN hardware. Unfortunately, these ANN-based systems are increasingly vulnerable to both transient and permanent faults due to unrelenting advances in CMOS technology scaling, which sometimes can be catastrophic. The considerable resource and energy consumption and the lack of dynamic adaptability make conventional fault-tolerant techniques unsuitable for future portable medical solutions. Inspired by the self-healing and self-recovery mechanisms of human nervous system, this research seeks to address reliability issues of ANN-based hardware by proposing an Autonomously Reconfigurable Artificial Neural Network (ARANN) architectural framework. Leveraging the homogeneous structural characteristics of neural networks, ARANN is capable of adapting its structures and operations, both algorithmically and microarchitecturally, to react to unexpected neuron failures. Specifically, we propose three key techniques --- Distributed ANN, Decoupled Virtual-to-Physical Neuron Mapping, and Dual-Layer Synchronization --- to achieve cost-effective structural adaptation and ensure accurate system recovery. Moreover, an ARANN-enabled self-optimizing workflow is presented to adaptively explore a "Pareto-optimal" neural network structure for a given application, on the fly. Implemented and demonstrated on a Virtex-5 FPGA, ARANN can cover and adapt 93% chip area (neurons) with less than 1% chip overhead and O(n) reconfiguration latency. A detailed performance analysis has been completed based on various recovery scenarios