Temporarily Fine-Grained Sleep Technique for Near- and Subthreshold Parallel Architectures

Backgate biasing is a promising technique for highspeed systems. Leakage can be reduced during standby periods by reverse bias while adequate bias in active mode can balance process and temperature variations. This technique introduces no delay penalty in active mode but slow wake up time results in system performance degradation. In this paper, a backgate bias accelerator achieving 24ns/V sleep-to-active mode transition rate is demonstrated in a 90 nm CMOS technology. The circuit performs auto-calibration of the transition time as a function of the Sleep and Active mode backgate bias voltages. Those can therefore be tuned on-chip according to process variations and/or operating conditions. The accelerator occupies less than 2.5% of the total chip area, consumes 600μW during the transitions and doesn’t add any bias current during active and sleep modes.

In this article, we consider a stochastic model of wireless sensor networks (WSNs) in which each sensor node randomly and alternatively stays in an active mode or a sleep mode. The active mode consists of two phases, called the full-active phase and the semi-active phase. When a referenced sensor node is in the full-active phase of the active mode, it may sense data packets, transmit the sensed packets, receive packets, and relay the received packets. However, when the phase of the sensor node switches from the full active phase to the semi-active phase, it is only able to transmit/relay data. When the referenced sensor node is in a sleep mode, it does not interact with the external world. In this article, first, we develop a stochastic model for the sensor node of a WSN, and then we derive an explicit expression of the stationary distribution of the number of data packets in the sensor node. Furthermore, we figure out some important performance measures, including the sensor node’s energy consumption for transmission, the energy consumption of the sensor operations, and the average energy consumption of the sensor node in a cycle of active and sleep modes. Also, a numerical analysis is provided to validate the proposed model and the results obtained. The novel aspects of our research are the development of a stochastic model for WSN with active and sleep features and the development of important analytical formulae for evaluating the energy consumption of a WSN. These results are expected to be useful as significant contributions to the fundamental theory of the design of various WSNs with active and sleep mode considerations.

This work presents a parallel context-modeling coding architecture and a matching arithmetic coder (MQ coder) for the embedded block coding (EBCOT) unit of the JPEG2000 encoder. The Tier-1 of the EBCOT consumes most of the time in a JPEG2000 encoding system. The proposed parallel architecture can increase the throughput rate of the context-modeling. To match the high throughput rate of the parallel context-modeling architecture, an efficient pipelined architecture for context-based adaptive arithmetic encoder is proposed. This encoder of JPEG2000 can work at 185MHz to encode one symbol each cycle. To compare to the conventional context-modeling architecture the presented parallel architecture can improve the throughput rate up to 25%. Because of the high throughput rate of the parallel context-modeling architecture, the concept of multi-rate processing for low power design can be applied.

A novel architecture for computing On-line clustering using moving average method for handling varied dimension data up to eight is proposed. The architecture proposed can perform clustering operation in a single clock cycle for any given dimension. A new method for division is proposed using parallel multiplier architecture and power of two which computes the division operation in single clock cycle. The architecture is tested for its working using Xilinx/ISim tool and the design is implemented using FPGA Spartan 3A.

The mobile IEEE 802.16e (WiMAX) environment has been considered a promoting wireless video multicasting technology. For example, mobile digital TV, which is one of video streaming service, can be supported in this environment. In order to support energy-consuming service like this, we need to operate some energy-saving mechanisms in the environment. In the IEEE 802.16e standard, some energy-saving mechanisms called “sleep mode operations” have been defined. Sleep mode operations can make connections between base station (BS) and mobile station (MS) enter sleep mode. While all of the connections enter sleep mode, MS can enter an energy-saving state. But in the standard, for real-time connections, it does not mention that how to switch a connection between awake mode and sleep mode. And the sleep mode operation for real-time connections can be more efficient about energy-saving and the Quality of Service (QoS) can be higher. So we have proposed “Two-Timer Mechanism” which can make real-time connections switch between awake mode and sleep mode, upgrade the efficiency of sleep mode operation, and improve the QoS. In this thesis, after a general structure of WiMAX MAC layer is analyzed, our Two-Timer Mechanism is designed and implemented in media access control (MAC) layer of WiMAX BS on the C platform provided by Institute for Information Industry (III). The conformance testing shows that MOB-SLP-RSPs conform to the standard. So it can work sleep mode operations on real-time connections successfully.

Sleep modes are emerging as a promising technique for energy-efficient networking: by adequately putting to sleep and waking up network resources according to traffic demands, a proportionality between energy consumption and network utilization can be approached, with important reductions in energy consumption. Previous studies have investigated and evaluated sleep modes for wireless access networks, computing variable percentages of energy savings. In this paper we characterize the maximum energy saving that can be achieved in a cellular wireless access network under a given performance constraint. In particular, our approach allows the derivation of realistic estimates of the energy-optimal density of base stations corresponding to a given user density, under a fixed performance constraint. Our results allow different proposals to be measured against the maximum theoretically achievable improvement. We show, through numerical evaluation and simulation, the possible energy savings in today's networks, and we further demonstrate that even with the development of highly energy-efficient hardware, a holistic approach incorporating system level techniques is essential to achieving maximum energy efficiency.

We propose a new dynamic bandwidth allocation algorithm for energy efficiency in next generation optical access (NGOA) networks, and evaluate the power savings possible at the optical network unit (ONU) by applying sleep and doze modes. Sleep mode is found to be most effective for NGOA systems with burst mode traffic transmission and reception.

Commodity HardWare (CHW) is currently used in the Internet to deploy large data centers or small computing nodes. Moreover, CHW will be also used to deploy future telecommunication networks, thanks to the adoption of the forthcoming network softwarization paradigm. In this context, CHW machines can be put in Active Mode (AM) or in Sleep Mode (SM) several times per day, based on the traffic requirements from users. However, the transitions between the power states may introduce fatigue effects, which may increase the CHW maintenance costs. In this paper, we perform a measurement campaign of a CHW machine subject to power state changes introduced by SM. Our results show that the temperature change due to power state transitions is not negligible, and that the abrupt stopping of the fans on hot components (such as the CPU) tends to spread the heat over the other components of the CHW machine. In addition, we also show that the CHW failure rate is reduced by a factor of 5 when the number of transitions between AM and SM states is more than 20 per day and the SM duration is around 800 [s].

On the effectiveness of single and multiple base station sleep modes in cellular networks

Data-retention flip-flop (DRFF) efficiently maintains data during sleep mode and retains state during transitions between active and sleep mode. This paper proposes a novel source-biased stacked inverter (SBS-Inverter) and a low-leakage, structure-reused DRFF. The sleep latch circuit constructed using the SBS-Inverter can effectively reduce the power of DRFF when storing data. Reuse of the structure improves the situation of redundant transistors in certain DRFF. Fine-grained inverter level optimization reduces delay and power during the active mode. The DRFF was implemented using a 55 nm process and subjected to comprehensive analysis. Post-layout simulation results at a supply voltage of 0.4 V indicate that the proposed DRFF’s data retention leakage power is only 5.3 pW. At a supply voltage of 0.8 V, the power-delay product is only 0.146 nW*ns@20 MHz. Monte Carlo simulation results considering process, voltage and temperature (PVT) variations show that the proposed DRFF can operate reliably down to a supply voltage of 0.4 V.

Android users are occasionally troubled by the slow UI responses and sudden application/OS crashes. These problems are mainly caused by software aging, a phenomenon of progressive degradation of performance and dependability typically observed in long-running software systems. A countermeasure to software aging is software rejuvenation, i.e., manual or scheduled restart at different levels, such as application, OS, and device. Various software aging and rejuvenation models have been proposed for different software systems. However, these traditional models cannot be applied in the context of mobile devices, as they seldom consider the patterns of usage behavior and user experience specific to mobile phones. We address this problem based on the observations that the usage time of mobile phones is typically fragmented in daily life, with frequent and periodical switches between active and sleep modes, and that the user experience on fluent operation in the active mode is a key concern for mobile users. These insights are exploited to model the usage behavior and aging process by individual Stochastic Petri-Nets, and then to compose them into a Continuous Time Markov Chain (CTMC). Furthermore, we propose proactive rejuvenation strategies based on such CTMCs to achieve the best user experience and the least user interference, such as restarting the device when it is in sleep mode and before it enters an aged state. To consider user experience - a key concern of mobile users which is still less prominent in traditional dependability measurements – we propose new related metrics: for fluency (i.e., the probability that a phone offers a fast UI response to the users), and for the degree of overall user experience. We demonstrate the effectiveness and advantages of the proposed models and metrics via simulations as well as an empirical study.

Power saving is one of the important issues for battery-powered mobile station in mobile WiMAX. Both IEEE 802.16e and IEEE 802.16m standards define sleep mode operations for power saving of mobile stations. In IEEE 802.16e, sleep mode alternates the listening window of fixed length and the sleep window where the sleep window can be doubled. The mobile station sends or receives packets during the active mode. In IEEE 802.16m, sleep cycle consists of extendable listening window and sleep window where sleep cycle can be doubled and the sleep window is the remaining part of sleep cycle. The mobile station sends or receives the data during the extendable listening window without going back to the active mode. The extendable listening window is implemented by T_AMS timer which plays the role of sleep mode request/response messages in the IEEE 802.16e. In this paper, we propose an efficient sleep mode operation for IEEE 802.16m advanced mobile WiMAX. The proposed scheme takes advantages of sleep modes in both IEEE 802.16e and IEEE 802.16m. This scheme has binary exponential sleep windows which guarantee the minimum length for effective power saving. The mobile station uses the T_AMS timer in IEEE 802.16m so that the mobile station sends or receives data packets during the extendable listening window in the sleep mode. We mathematically analyze the proposed scheme by an embedded Markov chain to obtain the average message delay and the average power consumption of a mobile station. The analytical results match with the simulation results very well. The analytical results show that the power consumption of our scheme is better than those of the legacy sleep modes in the IEEE 802.16e and the IEEE 802.16m under the same delay bound.

Energy usage in our networks is increasing rapidly; and to conserve energy, progress has been made in designing energy-efficient passive optical networks, which are being widely deployed for broadband access. Among the various energy-saving techniques, enabling sleep mode in optical network units is a very promising approach. However, the slow transition of power from active mode to sleep mode in an optical network unit, and the relatively large recovery and synchronization time needed during the wakeup process are challenges that need to be addressed. In this regard, we propose a service-level-agreement-based scheduling scheme for passive optical networks in which the optical line terminal can adjust the sleep time and the optical network unit can quit sleep mode for sending high-priority packets. The trade-off in energy savings vs. delay performance is evaluated using simulations under practical power consumption settings.

In this paper, we propose a sleep strategy for energy-efficient 5G Base Stations (BSs) with multiple Sleep Mode (SM) levels to bring down energy consumption. Such management of energy savings is coupled with managing the Quality of Service (QoS) resulting from waking up sleeping BSs. As a result, a tradeoff exists between energy savings and delay. Unlike prior work that studies this problem for binary state BS (ON and OFF), this work focuses on multi-level SM environment, where the BS can switch to several SM levels. We propose a Q-Learning algorithm that controls the state of the BS depending on the geographical location and moving velocity of neighboring users in order to learn the best policy that maximizes the tradeoff between energy savings and delay. We evaluate the performance of our proposed algorithm with an online suboptimal algorithm that we introduce as well. Results show that the Q-Learning algorithm performs better with energy savings up to 92% as well as better delay performance than the heuristic scheme.

This paper considers the design of wireless sensor networks in which a set of smart battery-powered sensor nodes cooperatively form an ad hoc communications network for monitoring and control applications. The paper examines ways of extending the life of such networks by introducing a 'sleep mode' in the sensor node. Quantitative analysis is used to show that although there is a complex relationship between 'sleeping' and energy conservation, it is possible to make significant energy savings while incurring only modest degradation in performance. An optimum energy saving curve is derived which provides a basis for the design of extended-life ad hoc wireless sensor networks.