Showing papers by "Shahin Nazarian published in 2014"

Dynamic thermal management for FinFET-based circuits exploiting the temperature effect inversion phenomenon

Abstract: Due to limits on the availability of the energy source in many mobile user platforms (ranging from handheld devices to portable electronics to deeply embedded devices) and concerns about how much heat can effectively be removed from chips, minimizing the power consumption has become a primary driver for system-on-chip designers. Because of their superb characteristics, FinFETs have emerged as a promising replacement for planar CMOS devices in sub-20nm CMOS technology nodes. However, based on extensive simulations, we have observed that the delay vs. temperature characteristics of FinFET-based circuits are fundamentally different from that of the conventional bulk CMOS circuits, i.e., the delay of a FinFET circuit decreases with increasing temperature even in the super-threshold supply voltage regime. Unfortunately, the leakage power dissipation of the FinFET-based circuits increases exponentially with the temperature. These two trends give rise to a tradeoff between delay and leakage power as a function of the chip temperature, and hence, lead to the definition of an optimum chip temperature operating point (i.e., one that balances concerns about the circuit speed and power efficiency.) This paper presents the results of our investigations into the aforesaid temperature effect inversion (TEI) and proposes a novel dynamic thermal management (DTM) algorithm, which exploits this phenomenon to minimize the energy consumption of FinFET-based circuits without any appreciable performance penalty. Experimental results demonstrate 40% energy saving (with no performance penalty) can be achieved by the proposed TEI-aware DTM approach compared to the best-in-class DTMs that are unaware of this phenomenon.

An electricity trade model for microgrid communities in smart grid

Abstract: Distributed microgrid network is the major trend of future smart grid, which contains various kinds of renewable power generation centers and a small group of energy users. In the distributed power system, each microgrid acts as a “prosumer” (producer and consumer) and maximizes its own social welfare. In addition, different microgrids can interact among each other through trading over a marketplace. In this paper, two models are introduced for microgrids to deal with the welfare maximization problems. In the first model, a microgrid is considered as a closed economy group and decides the optimal power generation distribution in terms of time. In the second model, each microgrid can trade with its neighborhoods and thus achieve a welfare increase from making use of its comparative advantage on power generation during a certain period of time. For each model, an efficient solution is presented. Experimental result shows the accuracy and efficiency of our presented solutions.

7nm FinFET standard cell layout characterization and power density prediction in near- and super-threshold voltage regimes

Abstract: In this paper, we present a power density analysis for 7nm FinFET technology node, including both near-threshold and super-threshold operations. We first build a Liberty-formatted standard cell library by selecting the appropriate number of fins for the pull-up and pull-down networks of each logic cell. The layout of each cell then is characterized based on the lambda-based layout design rules for FinFET devices. Finally, the power density of the 7nm FinFET technology node is analyzed and compared with the state-of-the-art 45nm CMOS technology node for different circuits. Hspice results show that the power density of each 7nm FinFET circuit is at least 10 to 20 times larger than that of the same 45nm CMOS circuit in near- and super-threshold voltage regimes. Also the power densities of FinFET circuits are shown to be much higher than the limit of air cooling, which necessitates careful thermal management for the FinFET technology.

Negotiation-based task scheduling to minimize user’s electricity bills under dynamic energy prices

Abstract: Dynamic energy pricing is a promising technique in the Smart Grid that incentivizes energy consumers to consume electricity more prudently in order to minimize their electric bills meanwhile satisfying their energy requirements. This has become a particularly interesting problem with the introduction of residential photovoltaic (PV) power generation facilities. This paper addresses the problem of task scheduling of (a collection of) energy consumers with PV power generation facilities, in order to minimize the electricity bill. A general type of dynamic pricing scenario is assumed where the energy price is both time-of-use and total power consumption-dependent. A negotiation-based iterative approach has been proposed that is inspired by the state-of-the-art Field-Programmable Gate Array (FPGA) routing algorithms. More specifically, the negotiation-based algorithm is used to rip-up and re-schedule all tasks in each iteration, and the concept of congestion is effectively introduced to dynamically adjust the schedule of each task based on the historical scheduling results as well as the (historical) total power consumption in each time slot. Experimental results demonstrate that the proposed algorithm achieves up to 51.8% improvement in electric bill reduction compared with baseline methods.

An improved logical effort model and framework applied to optimal sizing of circuits operating in multiple supply voltage regimes

Abstract: Digital near-threshold logic circuits have recently been proposed for applications in the ultra-low power end of the design spectrum, where the performance is of secondary importance. However, the characteristics of MOS transistors operating in the near-threshold region are very different from those in the strong-inversion region. This paper first derives the logical effort and parasitic delay values for logic gates in multiple voltage (sub/near/super-threshold) regimes based on the transregional model. The transregional model shows higher accuracy for both sub- and near-threshold regions compared with the subthreshold model. Furthermore, the derived near-threshold logical effort method is subsequently used for delay optimization of circuits operating in both near- and super-threshold regimes. In order to achieve this goal, a joint optimization of transistor sizing and adaptive body biasing is proposed and optimally solved using geometric programming. Experimental results show that our improved logical effort-based optimization framework provides a performance improvement of up to 40.1% over the conventional logical effort method.

Semi-analytical current source modeling of FinFET devices operating in near/sub-threshold regime with independent gate control and considering process variation

Abstract: Operating circuits in the near/sub-threshold regime can lower the circuit energy consumption at the expense of lowering the circuit speed. In addition near/sub-threshold can result in higher sensitivity to process-induced variations and transient noise. FinFETs have been proposed as an alternative to planar CMOS devices in sub-20nm CMOS technology nodes due to their more effective channel control, steep sub-threshold slope, high ON/OFF current ratio, low power consumption, and so on. Characteristics of FinFETs operating in the near/sub-threshold regime make it difficult to verify the timing of a circuit using conventional statistical static timing analysis (SSTA) techniques. Current source modeling (CSM) methods, which have been proposed to increase the accuracy of timing analysis in dealing with arbitrary shapes of the input signal waveforms, are the appropriate solution for performing SSTA on FinFET-based circuits. This paper thus extends the CSM to such circuits, operating in the near/sub-threshold voltage regime. In particular, FinFET devices with independent gate control and subject to process variations are modelled. The key idea of the proposed CSM approach is to combine non-linear analytical models and low-dimensional CSM lookup tables to simultaneously achieve high modeling accuracy and low time/space complexity.

An electricity trade model for multiple power distribution networks in smart energy systems

Abstract: The future smart energy systems are projected to be decentralized power networks, each consisting of various types of renewable power generators that serve a small group of energy users. Interaction between different power networks through energy trading over a marketplace provides the chance to fully utilize the capacity of each power generator type. As a result of this interaction, the power generation and distribution levels can be decided for each time slot in order to achieve a maximal utility. In this paper, an electricity trade model is introduced for decentralized power networks to deal with the utility maximization problem. In the proposed model, multiple power networks can trade among each other and thus each of them can achieve a utility increase from making use of its comparative advantage on power generation during a certain period of time. The model is studied from several special scenarios to a more general scenario and an efficient solution is presented for each scenario. Experimental result validates the accuracy and efficiency of the presented solutions.

An efficient semi-analytical current source model for FinFET devices in near/sub-threshold regime considering multiple input switching and stack effect

Abstract: Nanoscale FinFET devices are emerging as the transistor of choice in 32nm CMOS technologies and beyond. This is due to their more effective channel control, higher ON/OFF current ratios, and lower energy consumption. This paper presents an efficient current source model (CSM) for FinFET devices operating in the near/sub-threshold regime, considering multiple input switching (MIS) and accounting for the effect of internal node voltages of the logic cell. The main problem of the traditional MIS model is that it requires high-dimensional lookup tables. In this paper, we combine non-linear analytical models and low-dimensional CSM lookup tables to simultaneously achieve high modeling accuracy and time/space efficiency. The proposed framework is verified by experimental results on the 32nm Predictive Technology Model for FinFET devices.

A Probability Theory Based Price Determination Framework for Utility Companies in an Oligopolistic Energy Market

Abstract: Distributed power generation and distribution network with the dynamic pricing scheme are the major trend of the future smart grid. A smart grid is a network which contains multiple non-cooperative utility companies that offer time-of-use dependent energy prices to energy consumers and aim to maximize their own profits. Decentralized power network allows each energy consumer to have multiple choices among different utility companies. In this paper, an optimization framework is introduced to determine the energy price for utility companies in an oligopolistic energy market. At the beginning of each billing period (a day), each utility company will announce the time-of-use dependent pricing policy during the billing period, and each energy consumer will subsequently choose a utility company for energy supply to minimize the expected energy cost. The energy pricing competition among utility companies forms an n-person game because the pricing strategy of each utility company will affect the profits of others. To be realistic, the prediction error of a user's energy consumption is properly accounted for in this paper and is assumed to satisfy certain probability distribution at each time slot. We start from the most commonly-used normal distribution and extend our optimization framework to a more general case. A Nash equilibrium-based pricing policy is presented for the utility companies and the uniqueness of Nash equilibrium is proved. Experimental results show the effectiveness of our game theoretic price determination framework.

Energy optimal sizing of FinFET standard cells operating in multiple voltage regimes using adaptive independent gate control

Abstract: FinFET has been proposed as an alternative for bulk CMOS in the ultra-low power designs due to its more effective channel control, reduced random dopant fluctuation, higher ON/OFF current ratio, lower energy consumption, etc. The characteristics of FinFETs operating in the sub/near-threshold region are very different from those in the strong-inversion region. This paper introduces an analytical transregional FinFET model with high accuracy in both subthrehold and near-threshold regions. The unique feature of independent gate controls for FinFET devices is exploited for achieving a tradeoff between energy consumption and delay, and balancing the rise and fall times of FinFET gates. This paper proposes an effective design framework of FinFET standard cells based on the adaptive independent gate control method such that they can operate properly at all of subthreshold, near-threshold and super-threshold regions. The optimal voltage for independent gate control is derived so as to achieve equal rise and fall times or minimal energy-delay product at any supply voltage level.