An On-line BIST RAM Architecture with Self Repair Capabilities

The emerging field of self-repair computing is expected to have a major impact on deployable systems for space missions and defense applications, where high reliability, availability, and serviceability are needed. In this context, RAM (random access memories) are among the most critical components. This paper proposes a built-in self-repair (BISR) approach for RAM cores. The proposed design, introducing minimal and technology-dependent overheads, can detect and repair a wide range of memory faults including: stuck-at, coupling, and address faults. The test and repair capabilities are used on-line, and are completely transparent to the external user, who can use the memory without any change in the memory-access protocol. Using a fault-injection environment that can emulate the occurrence of faults inside the module, the effectiveness of the proposed architecture in terms of both fault detection and repairing capability was verified. Memories of various sizes have been considered to evaluate the area-overhead introduced by this proposed architectur

Quantum Hopfield neural network

Quantum computing allows for the potential of significant advancements in both the speed and the capacity of widely used machine learning techniques. Here we employ quantum algorithms for the Hopfield network, which can be used for pattern recognition, reconstruction, and optimization as a realization of a content-addressable memory system. We show that an exponentially large network can be stored in a polynomial number of quantum bits by encoding the network into the amplitudes of quantum states. By introducing a classical technique for operating the Hopfield network, we can leverage quantum algorithms to obtain a quantum computational complexity that is logarithmic in the dimension of the data. We also present an application of our method as a genetic sequence recognizer.Comment: 13 pages, 3 figures, final versio

Stream specificity and asymmetries in feature binding and content-addressable access in visual encoding and memory

YesHuman memory is content addressable—i.e., contents of the memory can be accessed using partial information about the bound features of a stored item. In this study, we used a cross-feature cuing technique to examine how the human visual system encodes, binds, and retains information about multiple stimulus features within a set of moving objects. We sought to characterize the roles of three different features (position, color, and direction of motion, the latter two of which are processed preferentially within the ventral and dorsal visual streams, respectively) in the construction and maintenance of object representations. We investigated the extent to which these features are bound together across the following processing stages: during stimulus encoding, sensory (iconic) memory, and visual shortterm memory. Whereas all features examined here can serve as cues for addressing content, their effectiveness shows asymmetries and varies according to cue–report pairings and the stage of information processing and storage. Position-based indexing theories predict that position should be more effective as a cue compared to other features. While we found a privileged role for position as a cue at the stimulus-encoding stage, position was not the privileged cue at the sensory and visual short-term memory stages. Instead, the pattern that emerged from our findings is one that mirrors the parallel processing streams in the visual system. This stream-specific binding and cuing effectiveness manifests itself in all three stages of information processing examined here. Finally, we find that the Leaky Flask model proposed in our previous study is applicable to all three features

GPUs as Storage System Accelerators

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201

Nanoscale content-addressable memory

A combined content addressable memory device and memory interface is provided. The combined device and interface includes one or more one molecular wire crossbar memories having spaced-apart key nanowires, spaced-apart value nanowires adjacent to the key nanowires, and configurable switches between the key nanowires and the value nanowires. The combination further includes a key microwire-nanowire grid (key MNG) electrically connected to the spaced-apart key nanowires, and a value microwire-nanowire grid (value MNG) electrically connected to the spaced-apart value nanowires. A key or value MNGs selects multiple nanowires for a given key or value

Quantum pattern recognition with liquid-state nuclear magnetic resonance

A novel quantum pattern recognition scheme is presented, which combines the idea of a classic Hopfield neural network with adiabatic quantum computation. Both the input and the memorized patterns are represented by means of the problem Hamiltonian. In contrast to classic neural networks, the algorithm can return a quantum superposition of multiple recognized patterns. A proof of principle for the algorithm for two qubits is provided using a liquid state NMR quantum computer.Comment: updated version, Journal-ref adde