The sentence-level extracted summary is inevitably mixed with redundant information due to the uninformative phrases or detailed expressions mixed in it. The extraction of fine-grained units is dedicated to retain the semantical integrity. To keep the balance between text redundancy and semantical integrity, we propose a novel clause-aware summarization model (TDCL-ClauseSum). We separate complex sentences into grammatically independent but semantically dependent clauses. The clause is regarded as the extraction unit and leverage graph neural network and topical information to capture clause-level relationship. Then a decoupled contrastive loss is stacked over the neural model to fill the gap between topic prediction and clause classification. The experiments of TDCL-ClauseSum are evaluated on two public benchmark datasets CNN/daily mail and New York Times, which contain 310574 and 150536 samples, respectively. Various experiments show that our method achieves remarkable performance on the two datasets (CNN/daily mail:43.94/20.65/40.75, New York Times:49.69/29.84/43.01, in ROUGE-1/ROUGE-2/ROUGE-L). Its promising performance demonstrates that the superiority of clause extraction.

The STT-MRAM technology is a promising candidate for future on-chip cache memory because of its high density, low standby power, and nonvolatility. As the technology node scales, especially under 40-nm technology node, STT-MRAM cell design becomes a key issue to approach low power consumption, high access performance, and desirable reliability. The conventional 1T-1 magnetic tunnel junction (MTJ) and 2T-2MTJ cell designs cannot address these challenges efficiently. In this paper, we propose a novel 3T-3MTJ cell structure using the advanced perpendicular MTJ (p-MTJ) technology. It can store 2 bits with three MTJs. The differential sensing technique can be used to read out the most significant bit as fast as the 2T-2MTJ design. The sensing latency of 2 bits within the same cell is almost the same as the sensing latency of the 1T-1MTJ cell design. Therefore, the 3T-3MTJ cell can have the advantages of both 2T-2MTJ and 1T-1MTJ cells. Circuit-level simulations show that the proposed 3T-3MTJ cell structure can achieve a desirable tradeoff between storage density, access performance, and energy consumption compared to the prior 1T-1MTJ and 2T-2MTJ cell structures. Additionally, we propose a novel adaptive cache design based on the 3T-3MTJ cell structure, which can work in different modes to satisfy various memory access demands from different applications. Architecture level simulations validate the effectiveness of the proposed cache design.

The STT-RAM (Spin-Transfer Torque Magnetic RAM) technology is a promising candidate for cache memory because of its high density, low standy-power, and non-volatility. As technology scales, especially under 40nm technology node, the read disturbance becomes severe since the read current approaches closely to the switching current. In addition, the read latency and access performance degrade significantly as well. The conventional 1T-1MTJ and 2T-2MTJ cell designs cannot address these challenges efficiently. In this paper, we propose a novel 3T-3MTJ cell structure using the advanced perpendicular MTJ technology. This memory cell has higher storage density and better performance, and is particularly suitable for the deeply scaled technology node. A two-stage sensing scheme is also proposed to facilitate the read operation of the 3T-3MTJ cell design. Circuit-level and architecture-level simulations show that the proposed 3T-3MTJ cell structure can achieve a better tradeoff between storage density, access performance, energy consumption, and reliability compared to the prior 1T-1MTJ and 2T-2MTJ cell structures.

Spin-transfer torque magnetic random access memory (STT-MRAM) is a promising emerging memory technology due to its various advantageous features such as scalability, nonvolatility, density, endurance, and fast speed. However, the reliability of STT-MRAM is severely impacted by environmental disturbances because radiation strike on the access transistor could introduce potential write and read failures for 1T1MTJ cells. In this paper, a comprehensive approach is proposed to evaluate the radiation-induced soft errors spanning from device modeling to circuit level analysis. The simulation based on 3-D metal-oxide-semiconductor transistor modeling is first performed to capture the radiation-induced transient current pulse. Then a compact switching model of magnetic tunneling junction (MTJ) is developed to analyze the various mechanisms of STT-MRAM write failures. The probability of failure of 1T1MTJ is characterized and built as look-up-tables. This approach enables designers to consider the effect of different factors such as radiation strength, write current magnitude and duration time on soft error rate of STT-MRAM memory arrays. Meanwhile, comprehensive write and sense circuits are evaluated for bit error rate analysis under random radiation effects and transistors process variation, which is critical for performance optimization of practical STT-MRAM read and sense circuits.

Spin-transfer torque random access memory (STT-RAM) is a promising candidate for next-generation non-volatile memory technologies. It combines many attractive attributes such as nanosecond access time, high integration density, non-volatility, and good CMOS process compatibility. However, process variation continues to be a critical issue in the designs of STT-RAM and the derived spintronic logic. Besides the process-variation-induced persistent operation error, the non-persistent error that is incurred by the intrinsic thermal fluctuations of Magnetic Tunneling Junction (MTJ) devices significantly influences the spintronic circuit reliability. In this paper, we analyzed these two types of STT-RAM operation errors at both single cell and array levels. On the top of that, we quantitatively investigate the impacts of these errors on a nonvolatile spintronic flip-flop design. Some possible design techniques to reduce the operation error rate are also discussed. Our experimental results show that a statistical design technique must be adopted in spintronic memory and logic designs to achieve the desired operation reliability. We refer this technique as “probabilistic design”.

Spin-transfer torque random access memory (STT-RAM) has recently gained increased attentions from circuit design and architecture societies. Although STT-RAM offers a good combination of small cell size, nanosecond access time and non-volatility for embedded memory applications, the reliability of STT-RAM is severely impacted by device variations and environmental disturbances. In this paper, we develop a compact switching model for magnetic tunneling junction (MTJ), which is the data storage device in STT-RAM cells. By leveraging the capability to simulate the impacts of thermal and process variations on MTJ switching, our model is able to analyze the diverse mechanisms of STT-RAM write operation failures. Besides the impacts of thermal and process variation, the soft error induced by radiation striking on the access transistor is another important threat to the MTJ reliability. It can also be analyzed by using our model. The incurred computation cost of our model is much less than the conventional macro-magnetic model, and hence, enabling its applications in comprehensive STT-RAM reliability analysis and design optimizations.

Novel nonvolatile memory (NVM) technologies are gaining significant attention from semiconductor industry in the competition of universal memory development. However, as nanoscale devices, these emerging NVMs suffer from the intrinsic technology challenges such as large process variations. The importance of effective statistical approaches for yield estimation and robust design arises in the commercialization of the emerging nonvolatile memory technologies. In this paper, we used Spin-Transfer Torque Random Access Memory (STT-RAM) as an example to explain some new memory failures mechanisms we have to face in the emerging memory technologies. Then, we applied a mixture importance sampling methodology to enable yield-driven design and extended its application beyond memories to peripheral circuits and logic blocks. The goal of these discussions is to propose a universal statistical methodology to predict memory loss and enable robust design practices.