Embedded memory is extensively being used in SoCs, and is rapidly growing in size and density. It contributes to SoCs to have greater features, but at the expense of taking up the most area. Due to continuous scaling of nanoscale device technology, large area size memory introduces aging-induced faults and soft errors, which affects reliability. In-field test and repair, as well as ECC, can be used to maintain reliability, and recently, these methods are used together to form a combined approach, wherein uncorrectable words are repaired, while correctable words are left to the ECC. In this paper, we propose a novel in-field repair strategy that repairs uncorrectable words, and possibly correctable words, for an ECC-based memory architecture. It executes an adaptive reconfiguration method that ensures 'fresh' memory words are always used until spare words run out. Experimental results demonstrate that our strategy enhances reliability, and the area overhead contribution is small.

In this paper, we propose a design for testability method for test programs of software-based self-test using test program templates. Software-based self-test using templates has a problem of error masking where some faults detected in a test generation for a module are not detected by the test program synthesized from the test. The proposed method achieves 100% template level fault efficiency, that is, it completely avoids the error masking. Moreover, the proposed method has no performance degradation (adds only observation points) and enables at-speed testing.

We consider linearizable implementations of shared FIFO queues and general deterministic objects on a distributed message-passing system which provides a real-time timer. The efficiency of an implementation is measured by the worst-case response time res_time(op) for each operation op of the implemented objects. We show the following results under the assumption that all message delays are in the range [d-u,d] for some constants d and u (0 u d). We first present an implementation of deterministic objects with res_time(opa)=u for any ack-type operation opa and res_time(opv)=2d for any val-type operation opv, where an ack-type operation is an operation which always returns a unique response and a val-type operation is an operation which is not ack-type. We also consider an implementation of FIFO queues, which have two kinds of operations, enq(v) and deq. We show that, for any implementation of FIFO queues, (1) res_time(enq(v)) u(n-1)/n holds for some v where n is the number of processes, and (2) res_time(deq) d+u/2 holds in the case of u (2/3)d.

This paper proposes an efficient methodology of delay fault testing of processor cores using their instruction sets. These test vectors can be applied in the functional mode of operation, hence, self-testing of processor core becomes possible for path delay fault testing. The proposed approach uses a graph theoretic model (represented as an Instruction Execution Graph) of the datapath and a finite state machine model of the controller for the elimination of functionally untestable paths at the early stage without looking into the circuit details and extraction of constraints for the paths that can potentially be tested. Parwan and DLX processors are used to demonstrate the effectiveness of our method.

This paper proposes two power-constrained test synthesis schemes and scheduling algorithms, under non-scan BIST, for RTL data paths. The first scheme uses boundary non-scan BIST, and can achieve low hardware overheads. The second scheme uses generic non-scan BIST, and can offer some tradeoffs between hardware overhead, test application time and power dissipation. A designer can easily select an appropriate design parameter based on the desired tradeoff. Experimental results confirm the good performance and practicality of our new approaches.

We propose an algorithm for the gathering problem of mobile agents in arbitrary networks (graphs) with Byzantine agents. Our algorithm can make all correct agents meet at a single node in O(fm) time (f is the upper bound of the number of Byzantine agents and m is the number of edges) under the assumption that agents have unique ID and behave synchronously, each node is equipped with an authenticated whiteboard, and f is known to agents. Here, the whiteboard is a node memory where agents can leave information. Since the existing algorithm achieves gathering without a whiteboard in Õ(n9λ) time, where n is the number of nodes and λ is the length of the longest ID, our algorithm shows an authenticated whiteboard can significantly reduce the time for the gathering problem in Byzantine environments.

We propose two algorithms for the gathering of k mobile agents in asynchronous Byzantine environments. For both algorithms, we assume that graph topology is arbitrary, each node is equipped with an authenticated whiteboard, agents have unique IDs, and at most f weakly Byzantine agents exist. Here, a weakly Byzantine agent can make arbitrary behavior except falsifying its ID. Under these assumptions, the first algorithm achieves a gathering without termination detection in O(m+fn) moves per agent (m is the number of edges and n is the number of nodes). The second algorithm achieves a gathering with termination detection in O(m+fn) moves per agent by additionally assuming that agents on the same node are synchronized, $f

This paper proposes a low power scan test scheme and formulates a problem based on this scheme. In this scheme the flip-flops are grouped into N scan chains. At any time, only one scan chain is active during scan test. Therefore, both average power and peak power are reduced compared with conventional full scan test methodology. This paper also proposes a tabu search-based approach to minimize test application time. In this approach we handle the information during deterministic test efficiently. Experimental results demonstrate that this approach drastically reduces both average power and peak power dissipation at a little longer test application time on various benchmark circuits.

Statistical wafer-level characteristic variation modeling offers an attractive method for reducing the measurement cost in large-scale integrated (LSI) circuit testing while maintaining test quality. In this method, the performance of unmeasured LSI circuits fabricated on a wafer is statistically predicted based on a few measured LSI circuits. Conventional statistical methods model spatially smooth variations in the wafers. However, actual wafers can exhibit discontinuous variations that are systematically caused by the manufacturing environment, such as shot dependence. In this paper, we propose a modeling method that considers discontinuous variations in wafer characteristics by applying the knowledge of manufacturing engineers to a model estimated using Gaussian process regression. In the proposed method, the process variation is decomposed into systematic discontinuous and global components to improve estimation accuracy. An evaluation performed using an industrial production test dataset indicates that the proposed method effectively reduces the estimation error for an entire wafer by over 36% compared with conventional methods.

Nano-scale VLSI design is facing the problems of increased test data volume. Small delay defects are becoming possible sources of test escapes, and high delay test quality and therefore a greater volume of test data are required. The increased test data volume requires more tester memory and test application time, and both result in test cost inflation. Test pattern ordering gives a practical solution to reduce test cost, where test patterns are ordered so that more defects can be detected as early as possible. In this paper, we propose a test pattern ordering method based on SDQL (Statistical Delay Quality Level), which is a measure of delay test quality considering small delay defects. Our proposed method orders test patterns so that SDQL shrinks fast, which means more delay defects can be detected as early as possible. The proposed method efficiently orders test patterns with minimal usage of time-consuming timing-aware fault simulation. Experimental results demonstrate that our method can obtain test pattern ordering within a reasonable time, and also suggest how to prepare test sets suitable as inputs of test pattern ordering.

Distributed computation has attracted considerable attention and large-scale distributed systems have been designed and developed. A distributed system inherently has possibility of fault tolerance because of its redundancy. Thus, a great deal of investigation has been made to design fault-tolerant distributed algorithms. This paper introduces two promising paradigms, self-stabilization and wait-freedom, for designing fault-tolerant distributed algorithms and discusses some subjects important from the point of view of algorithm engineering.

We present a high-level synthesis scheme that considers weak testability of generated register-transfer level (RTL) data paths, as well as their area and performance. The weak testability, proposed in our previous work, is a testability measure of RTL data paths for non-scan design. In our scheme, we first extract a condition on resource sharing sufficient for weak testability from a data flow graph before synthesis, and treat the condition as design objectives in the following synthesis tasks. We propose heuristic synthesis algorithms which optimize area and the design objectives under the performance constraint.

We study the gathering problem requiring a team of mobile agents to gather at a single node in arbitrary networks. The team consists of k agents with unique identifiers (IDs), and f of them are weakly Byzantine agents, which behave arbitrarily except falsifying their identifiers. The agents move in synchronous rounds and cannot leave any information on nodes. If the number of nodes n is given to agents, the existing fastest algorithm tolerates any number of weakly Byzantine agents and achieves gathering with simultaneous termination in O(n4·|Λgood|·X(n)) rounds, where |Λgood| is the length of the maximum ID of non-Byzantine agents and X(n) is the number of rounds required to explore any network composed of n nodes. In this paper, we ask the question of whether we can reduce the time complexity if we have a strong team, i.e., a team with a few Byzantine agents, because not so many agents are subject to faults in practice. We give a positive answer to this question by proposing two algorithms in the case where at least 4f2+9f+4 agents exist. Both the algorithms assume that the upper bound N of n is given to agents. The first algorithm achieves gathering with non-simultaneous termination in O((f+|&Lambdagood|)·X(N)) rounds. The second algorithm achieves gathering with simultaneous termination in O((f+|&Lambdaall|)·X(N)) rounds, where |&Lambdaall| is the length of the maximum ID of all agents. The second algorithm significantly reduces the time complexity compared to the existing one if n is given to agents and |&Lambdaall|=O(|&Lambdagood|) holds.

We propose an attention mechanism in deep learning networks for gender recognition using the gaze distribution of human observers when they judge the gender of people in pedestrian images. Prevalent attention mechanisms spatially compute the correlation among values of all cells in an input feature map to calculate attention weights. If a large bias in the background of pedestrian images (e.g., test samples and training samples containing different backgrounds) is present, the attention weights learned using the prevalent attention mechanisms are affected by the bias, which in turn reduces the accuracy of gender recognition. To avoid this problem, we incorporate an attention mechanism called gaze-guided self-attention (GSA) that is inspired by human visual attention. Our method assigns spatially suitable attention weights to each input feature map using the gaze distribution of human observers. In particular, GSA yields promising results even when using training samples with the background bias. The results of experiments on publicly available datasets confirm that our GSA, using the gaze distribution, is more accurate in gender recognition than currently available attention-based methods in the case of background bias between training and test samples.

Due to outsourcing of numerous stages of the IC manufacturing process to different foundries, the security risk, such as hardware Trojan becomes a potential threat. In this paper, we present a layout aware localized hardware Trojan detection method that magnifies the detection sensitivity for small Trojan in power-based side-channel analysis. A scan segmentation approach with a modified launch-on-capture (LoC) transition delay fault test pattern application technique is proposed so as to maximize the dynamic power consumption of any target region. The new architecture allows activating any target region and keeping others quiet, which reduces total circuit toggling activity. We evaluate our approach on ISCAS89 benchmark and two practical circuits to demonstrate its effectiveness in side-channel analysis.

Register-transfer-level (RTL) information is hardly available for hardware Trojan detection. In this paper, four RTL Trojan features related to branching statement are proposed. The Minimum Redundancy Maximum Relevance (mRMR) feature selection is applied to the proposed Trojan features to determine the recommended feature combinations. The feature combinations are then tested using different machine learning concepts in order to determine the best approach for classifying Trojan and normal branches. The result shows that a Decision Tree classification algorithm with all the four proposed Trojan features can achieve an average true positive detection rate of 93.72% on unseen test data.

In this paper, we present two parallel algorithms for computing the all nearest neighbors of an n n binary image on the Bulk-Synchronous Parallel(BSP) model. The BSP model is an asynchronous parallel computing model, where its communication features are abstracted by two parameters L and g: L denotes synchronization periodicity and g denotes a reciprocal of communication bandwidth. We propose two parallel algorithms for the all nearest neighbor problems based on two distance metrics. The first algorithm is for Lp distance, and the second algorithm is for weighted distance. Both two algorithms run in O(n2/p + L) computation time and in O(g(n/p) + L) communication time using p (1 p n) processors and in O(n2/p + (d+L)(log(p/n)/log(d+1))) computation time and in O(g(n/p) + (gd+L)(log(p/n)/log(d+1))) communication time using p (n< p n2) processors on the BSP model, for any integer d(1 dp/n).

We consider a wait-free linearizable implementation of shared objects on a distributed message-passing system. We assume that the system provides each process with a local clock that runs at the same speed as global time and that all message delays are in the range [d-u,d] where d and u (0< u d) are constants known to every process. We present four wait-free linearizable implementations of read/write registers on reliable and unreliable broadcast models. We also present two wait-free linearizable implementations of general objects on a reliable broadcast model. The efficiency of an implementation is measured by the worst-case response time for each operation of the implemented object. Response times of our wait-free implementations of read/write registers on a reliable broadcast model is better than a previously known implementation in which wait-freedom is not taken into account.

We investigate possibility of fault-tolerant and self-stabilizing protocols (ftss protocols) using an unreliable failure detector. Our main contribution is (1) to newly introduce k-accuracy of an unreliable failure detector, (2) to show that k-accuracy of a failure detector is necessary for any ftss k-group consensus protocol, and (3) to present three ftss k-group consensus protocols using a k-accurate and weakly complete failure detector under the read/write daemon on complete networks and on (n-k+1)-connected networks, and under the central daemon on complete networks.