Luca Negri

Power-efficient ASIC synthesis of cryptographic sboxes

In this paper we present a novel methodology that can be used to design efficient hardware structures for a certain class of combinatorial functions. The methodology is primarily intended to achieve low-power synthesis of non-linear one-to-one functions on ASIC technology libraries and fits well for the synthesis of small cryptographic substitution box (Sbox) functional components; the latter are found in most secret key cryptographic algorithms, and usually represent their most relevant part in terms of required computational power. We also describe an extension that allows us to apply the method to general vectorial Boolean functions.

FSM--based power modeling of wireless protocols: the case of bluetooth

The proliferation of pervasive computing applications relying on battery-powered devices and wireless connectivity is posing great emphasis on the issue of power optimization. While node-level models and approaches have been widely discussed, a problem requiring even greater attention is that of power associated with the communication protocols. We propose a high-level modeling methodology based on finite state machines useful to predict the energy consumption of given communication tasks with very low computational cost, which can be applied to any protocol. We use this methodology to create a power model of Bluetooth that we characterize and validate experimentally on a real implementation.

StateCharts to systemc: a high level hardware simulation approach

In this paper we present a tool that converts specifications written with a subset of StateCharts into SystemC behavioral models. The main advantages of such an approachare rapidity of use, simplicity and reusability. Various systems can be modeled at different levels of abstraction andaccuracy through StateCharts and different peculiar aspects (e.g. energy, performances) can be taken into consideration. Moreover different parts of the design can be identified at different detail levels. The kernel of the simulator is fully discussed together with its mapping to the semantics of our StateCharts diagrams. As a case study we present here a model of the IBM PowerPC 750 Cache system and the respective SystemC simulator automatically generated by our tool.

Power Modeling and Power Analysis for IEEE 802.15.4: a Concurrent State Machine Approach

is a recent low-rate/low-power standard for wireless personal area and sensor networks. Its simple infrastructure, intermediate range and good power performance make it a candidate for applications that require a reasonably low throughput but a very high device lifetime and power efficiency. An experimental power analysis of an 802.15.4 implementation is carried out, providing a detailed power model of the protocol based on concurrent state machines; resulting power model is then used to generate a customized simulator. The model has been validated through a set of experiments and provides good accuracy; results are discussed, considering in particular use of the model as a basis for subsequent optimizations on 802.15.4 networks.

Power/Performance Tradeoffs in Bluetooth Sensor Networks

Low power consumption is a critical issue in wireless sensor networks. Over the past few years, a considerable number of ad-hoc architectures and communication protocols have been proposed for sensor network nodes. If on one hand custom solutions carry the greatest power optimization potential, widespread communication standards guarantee interoperability and ease of connection with existing devices. In this paper we present a variable-granularity power model of Bluetooth, and apply it to variable-complexity optimization scenarios, to devise optimal power management policies. These policies, if backed by hardware implementations that are more power-aggressive than those available, could make the protocol fit for a wider range of sensor networks than it is today.

Flexible power modeling for wireless systems: power modeling and optimization of two Bluetooth implementations

Battery-powered mobile devices featuring wireless connectivity are becoming part of everyday life. In a functional breakdown of their power budget, communication accounts for a steadily increasing share of the total power; this is pushing research on power management of the wireless interfaces. The key to effective power management is power modeling. We describe a power modeling methodology featuring variable granularity and low computational burden, which can be applied to any wireless protocol. We apply it to Bluetooth and model two different BT modules, highlighting how different power management policies suit different implementations.

Application-level power management in pervasive computing systems: a case study

In this paper we propose an application-level power consumption modeling and optimization technique for mobile devices. The application being considered is modeled as a FSM and the power consumption figures are associated with it through current measurements on selected states, followed by the application of a linear functional model. The FSM model is then used, together with a power management policy, to extend battery lifetime while guaranteeing the execution of essential states in the application. In this paper, the methodology is applied to a specific case study, namely the fruition of multimedia content in an E-Learning scenario.

Power management for bluetooth sensor networks

Low power is a primary concern in the field of wireless sensor networks. Bluetooth has often been labeled as an inappropriate technology in this field due to its high power consumption. However, most Bluetooth studies employ rather over–simplified, fully theoretical, or inadequate power models. We present a power model of Bluetooth including scatternet configurations and low–power sniff mode and validate it experimentally on a real Bluetooth module. Based on this model, we introduce a power optimization framework employing MILP (Mixed–Integer Linear Programming) techniques, and devise optimal power management policies in the presence of end–to-end delay constraints. Our optimizations, if backed by power–aggressive hardware implementations, can make Bluetooth viable for a wider range of sensor networks.