In order to meet the diverse requirements of modern storage systems, flash memory should be optimized by precisely tuning a huge number of internal operating parameters. Although 3D NAND flash memory successfully increases the capacity of storage systems, its complex architecture and unique error behavior make such optimization a more difficult and time-consuming process during NAND manufacturing. This work introduces Norns, a novel method for post-fabrication optimization of NAND flash memory, which is an essential step in the manufacturing process of modern 3D NAND flash memory to simultaneously meet various requirements on reliability, performance, yield, etc. Norns is based on simple machine-learning approaches yet with three key guidelines that leverage (i) domain-specific rules, (ii) recent optimization results, and (iii) online simulation, respectively, to enable quick optimization of a large number of device parameters within the limited product turnaround time (TAT). We evaluate Norns in mass production for 7th-generation QLC NAND flash memory and 8th-generation TLC NAND flash memory. Our Norns can achieve superior optimization over existing post-fabrication optimization techniques by showing significant performance and reliability improvements by up to 8.8% and 12% on average, respectively.

The Internet of Bio-Nano Things (IoBNT) is an emerging area of networking where seamless interconnection between biological and electrical nanotechnology-enabled devices is envisioned to enable disruptive applications. This paper focuses on characterizing the propagation of information (communication channel) through an interface between the biological and the electrical domains, whose components are commonly used as part of biosensing systems. On top of its sensing capabilities, this interface, based on electrochemical transduction, is here innovatively modeled and characterized in terms of communication performance, a key parameter in the context of IoBNT. In particular, the transduction at the basis of this channel is enabled by interactions with a circuit electrode of reduction-oxidation (redox) chemical reactions. Redox reactions are among the building blocks of communication channels in biological cells and tissues. Based on the underlying physical and chemical processes, an analytical system model is detailed for the channel. Based on the latter, an experimentally validated computational model is formulated to enable the empirical estimation of its performance through simulation data. The estimation of Signal-to-Noise Ratio (SNR), Limit of Detection (LoD), and Limit of Quantification (LoQ) are followed by frequency-based analysis of the channel and the estimation of an upper-bound to its information capacity. Design rules based on a trade-off between the dimension of the transducer and its communication performance are derived to aid in the integration of this communication channel into future IoBNT devices.

With the growing complexity of integrated circuits and increasing capacity of memory devices, it is becoming increasingly difficult to screen out all defective dies through wafer test. To address this problem, statistical methods are being increasingly applied on test data to screen out potential defective dies. Among such methods, the good-die-in-bad-neighborhood (GDBN) method is effective. Most GDBN methods use the bin data of nearby dies through Cartesian coordinates. However, the bin data provide only binary information, and Cartesian coordinates do not fully reflect the spatial correlations of wafers. Therefore, this paper proposes a novel GDBN method using bad block data, which are key indicators of the in-field failure of NAND flash memory. The defect risk is predicted through statistical kernel smoothing based on Cartesian and polar coordinates to consider the spatial correlations in various environments of wafer fabrication. Experiments on NAND flash products show that the in-field failure classification performance of the proposed method, in terms of the area under the curve value, is 30% higher than that of the existing method, based on the bin data and Cartesian coordinates.

Biology is well-known for its ability to communicate through (i) molecularly-specific signaling modalities and (ii) a globally-acting electrical modality associated with ion flow across biological membranes. Emerging research suggests that biology uses a third type of communication modality associated with a flow of electrons through reduction/oxidation (redox) reactions. This redox signaling modality appears to act globally and has features of both molecular and electrical modalities: since free electrons do not exist in aqueous solution, the electrons must flow through molecular intermediates that can be switched between two states - with electrons (reduced) or without electrons (oxidized). Importantly, this global redox modality is easily accessible through its electrical features using convenient electrochemical instrumentation. In this review, we explain this redox modality, describe our electrochemical measurements, and provide four examples demonstrating that redox enables communication between biology and electronics. The first two examples illustrate how redox probing can acquire biologically relevant information. The last two examples illustrate how redox inputs can transduce biologically-relevant transitions for patterning and the induction of a synbio transceiver for two-hop molecular communication. In summary, we believe redox provides a unique ability to bridge bio-device communication because simple electrochemical methods enable global access to biologically meaningful information. Further, we envision that redox may facilitate the application of information theory to the biological sciences.

In this paper, we described the process of building a large-scale speech-to-text pipeline. Two target domains, daily conversations and travel-related conversations between two agents, for the English-German language pair (both directions) are examined. The SMT component is built from out-of-domain but freely-available bilingual and monolingual data. We make use of most of the known available resources to examine the effects of unrestricted data and large scale models. A naive baseline delivers solid results in terms of MT-quality. Extending the baseline with context-based translation model features like operations sequence models, higher-order class-based language models, and additional web-scale word-based language models leads to a system that significantly outperforms the baseline. Domain adaption is performed by separately weighting the influence of the out-of-domain subcorpora. This is explored for translation models and language models yielding significant improvements in both cases. Automatic and manual evaluation results are provided for raw MT-quality and ASR+MT-quality.

Performance and power issues are becoming increasingly important in the design of large, cluster-based multitier data centers for supporting a multitude of services. The design and analysis of such large/complex distributed systems often suffer from the lack of availability of an adequate physical infrastructure. This paper presents a comprehensive, flexible, and scalable simulation platform for in-depth analysis of multi-tier data centers. Designed as a pluggable three-level architecture, our simulator captures all the important design specifics of the underlying communication paradigm, kernel level scheduling artifacts, and the application level interactions among the tiers of a three-tier data center. The flexibility of the simulator is attributed to its ability in experimenting with different design alternatives in the three layers, and in analyzing both the performance and power consumption with realistic workloads. The scalability of the simulator is demonstrated with analyses of different data center configurations. In addition, we have designed a prototype three-tier data center on an Infiniband Architecture (IBA) connected Linux cluster to validate the simulator. Using RUBiS benchmark workload, it is shown that the simulator is quite accurate in estimating the throughput, response time, and power consumption. We then demonstrate the applicability of the simulator in conducting three different types of studies. First, we conduct a comparative analysis of the IBA and 10 Gigabit Ethernet (10GigE) under different traffic conditions and with varying size clusters for understanding their relative merits in designing cluster-based servers. Second, measurement and characterization of power consumption across the servers of a three-tier data center is done. Third, we perform a configuration analysis of the Web server (WS), Application Server (AS), and Database Server (DB) for performance optimization. We believe that such a comprehensive simulation infrastructure is critical for providing guidelines in designing efficient and cost-effective multi-tier data centers.

A multicodebook quantization method for improving the performance of the split vector quantization (SVQ) is described. In the proposed method, multiple codebooks having different sizes are trained, and the minimal-size codebook and its codeword satisfying the condition based on the spectral distortion are determined. Several clustering techniques for reducing total bit rate and the analysis of the computational complexity are also presented. Experimental results have shown that the proposed method reduces the number of outliers significantly and achieves a better rate-distortion performance compared with the fixed-bit-rate SVQ, multistage VQ, and variable-bit-rate SVQ.

A new band-splitting method for two-band speech model is proposed, whereby a score function based on the subband periodicity is calculated for each harmonic band and the band of maximum score is selected as the band-splitting frequency. Using the score function, a tracking technique is applied to the algorithm for the continuity of the band-splitting frequencies between neighboring frames. This method has proven to be robust to the inaccuracy of pitch estimation and the frequency resolution problem in low-pitched speech.

This paper proposes a new voice conversion technique based on hidden Markov model (HMM) for modeling of speaker’s dynamic characteristics. The basic idea of this technique is to use state transition probability as speaker’s dynamic characteristics and have conversion rule at each state of HMM. A couple of methods is developed for creating state-dependent conversion rule. One uses source speaker’s spectral dynamics and the other uses target speaker’s. The experimental results showed that the proposed methods have better performance than conventional VQ-method in both objective and subjective tests. The comparison of our two methods showed that the method using target speaker’s dynamics is superior in listening test and produces more natural sound.