| Title | Flexible Transition Metel Dichalcogenide Field-Effect Transistors: A Circuit-Level Simulation Study of Delay and Power under Bending, Process Variation, and Scaling |
| Author | Ying-Yu Chen (University of Illinois at Urbana-Champaign, U.S.A.), *Morteza Gholipour (Babol University of Technology, Iran), Deming Chen (University of Illinois at Urbana-Champaign, U.S.A.) |
| Page | pp. 761 - 768 |
| Keyword | transition metal dichalcogenide, TMDFET, flexible electronics, modeling, simulation |
| Abstract | In this paper, a new and efficient SPICE model of flexible transition metal dichalcogenide field-effect transistors (TMDFETs) is developed for different types of materials, considering effects when scaling the transistor size down to the 16-nm technology node. Extensive circuit-level simulations are performed using this model, and the delay and power performance of TMDFET circuits with different amounts of bending are reported. Simulation results indicate that delay and power trade-off can be done in TMDFET circuits via bending. Effects from process variation are also evaluated via circuit simulations. Finally, our cross-technology and scaling studies show that while TMDFETs perform better than Si-based transistors in terms of energy-delay product (EDP) at 180-nm and 90-nm technology nodes (the best being 12.7% and 40.7% of that of Si-based transistors, respectively), their EDPs are worse than Si-based transistors (at least 4.9x of that of the best performing Si-based transistor) on the 16-nm technology node. Such a compact model would enable SPICE-level circuit simulation for early assessment, design, and evaluation of futuristic TMDFET-based flexible circuits targeting advanced technology nodes. |
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| Title | Non-Volatile Non-Shadow Flip-Flop using Spin Orbit Torque for Efficient Normally-off Computing |
| Author | *Rajendra Bishnoi, Fabian Oboril, Mehdi B. Tahoori (Karlsruhe Institute of Technology, Germany) |
| Page | pp. 769 - 774 |
| Keyword | Spin orbit torque, low power, flip-flop, write avoidance, power gate |
| Abstract | With technology downscaling, it is very challenging to deal with static power. Conventional CMOS and Non-Volatile flip-flops cannot provide effective solution for such problem. We propose a novel Non-Volatile Non-Shadow flip-flop using Spin Orbit Torque based MTJ cells. In this design, we exploit the high speed, low energy and high reliability features of SOT devices to employ them as active components of the flip-flop. This enables efficient normally-off computing by allowing very aggressive power gating for both short and long standby periods. Experimental results show that the NVNS-FF has similar energy and timing characteristics as conventional CMOS-based flip-flops in active mode, and at the same time it allows to reduce the static power by 5X compared to backup flip-flops. |
| Title | Optimal Co-Scheduling of HVAC Control and Battery Management for Energy-Efficient Buildings Considering State-of-Health Degradation |
| Author | *Tiansong Cui, Shuang Chen (University of Southern California, U.S.A.), Yanzhi Wang (Syracuse University, U.S.A.), Qi Zhu (University of California, Riverside, U.S.A.), Shahin Nazarian, Massoud Pedram (University of Southern California, U.S.A.) |
| Page | pp. 775 - 780 |
| Keyword | HVAC Control, Battery, Smart Building |
| Abstract | The heating, ventilation and air conditioning (HVAC) system accounts for half of the energy consumption of a typical building. Additionally, the need for HVAC changes over hours and days as does the electric energy price. Level of comfort of the building occupants is, however, a primary concern, which tends to overwrite pricing. Dynamic HVAC control under a dynamic energy pricing model while meeting an acceptable level of occupants' comfort is thus critical to achieving energy efficiency in buildings in a sustainable manner. Finally, there is the possibility that the building is equipped with some renewable source of power such as solar panels mounted on the rooftop. The presence of a battery energy storage system in a target building would enable peak power shaving by adopting a suitable charge and discharge schedule for the battery, while simultaneously meeting building energy efficiency and user satisfaction. Achieving this goal requires detailed information (or predictions) about the amount of local power generation from the renewable source plus the power consumption load of the building. This paper addresses the co-scheduling problem of HVAC control and battery management to achieve energy-efficient buildings, while also accounting for the degradation of the battery state-of-health during charging and discharging operations (which in turn determines the amortized cost of owning and utilizing a battery storage system). A time-of-use dynamic pricing scenario is assumed and various energy loss components are considered including power dissipation in the power conversion circuitry as well as the rate capacity effect in the battery. A global optimization framework targeting the entire billing cycle is presented and an adaptive co-scheduling algorithm is provided to dynamically update the optimal HVAC air flow control and the battery charging/discharging decision in each time slot during the billing cycle to mitigate the prediction error of unknown parameters. Experimental results show that the proposed algorithm achieves up to 15% in the total electric utility cost reduction compared with some baseline methods. |
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| Title | Accurate Remaining Range Estimation for Electric Vehicles |
| Author | *Joonki Hong, Sangjun Park, Naehyuck Chang (Korea Advanced Institute of Science and Technology, Republic of Korea) |
| Page | pp. 781 - 786 |
| Keyword | Electric vehicle, Range estimation, Modeling methodology, EV power model, Regression |
| Abstract | EV drivers have range anxiety because of a short driving range of the EV. In this paper, we emphasize that accurate remaining range estimation can efficiently mitigate the range anxiety of EV drivers. Just like the analogous concepts used in the power estimation of digital circuits, remaining range estimation consists of the two consecutive steps, driving profile estimation and power consumption estimation. We come up with a hybrid modeling methodology, and decreased the estimation error down to 2.52%. |
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