An Efficient behavioral description frontend tool for mixed-mode SPICE simulation

Postprint (published version

Design Solutions For Modular Satellite Architectures

The cost-effective access to space envisaged by ESA would open a wide range of new opportunities and markets, but is still many years ahead. There is still a lack of devices, circuits, systems which make possible to develop satellites, ground stations and related services at costs compatible with the budget of academic institutions and small and medium enterprises (SMEs). As soon as the development time and cost of small satellites will fall below a certain threshold (e.g. 100,000 to 500,000 €), appropriate business models will likely develop to ensure a cost-effective and pervasive access to space, and related infrastructures and services. These considerations spurred the activity described in this paper, which is aimed at: - proving the feasibility of low-cost satellites using COTS (Commercial Off The Shelf) devices. This is a new trend in the space industry, which is not yet fully exploited due to the belief that COTS devices are not reliable enough for this kind of applications; - developing a flight model of a flexible and reliable nano-satellite with less than 25,000€; - training students in the field of avionics space systems: the design here described is developed by a team including undergraduate students working towards their graduation work. The educational aspects include the development of specific new university courses; - developing expertise in the field of low-cost avionic systems, both internally (university staff) and externally (graduated students will bring their expertise in their future work activity); - gather and cluster expertise and resources available inside the university around a common high-tech project; - creating a working group composed of both University and SMEs devoted to the application of commercially available technology to space environment. The first step in this direction was the development of a small low cost nano-satellite, started in the year 2004: the name of this project was PiCPoT (Piccolo Cubo del Politecnico di Torino, Small Cube of Politecnico di Torino). The project was carried out by some departments of the Politecnico, in particular Electronics and Aerospace. The main goal of the project was to evaluate the feasibility of using COTS components in a space project in order to greatly reduce costs; the design exploited internal subsystems modularity to allow reuse and further cost reduction for future missions. Starting from the PiCPoT experience, in 2006 we began a new project called ARaMiS (Speretta et al., 2007) which is the Italian acronym for Modular Architecture for Satellites. This work describes how the architecture of the ARaMiS satellite has been obtained from the lesson learned from our former experience. Moreover we describe satellite operations, giving some details of the major subsystems. This work is composed of two parts. The first one describes the design methodology, solutions and techniques that we used to develop the PiCPoT satellite; it gives an overview of its operations, with some details of the major subsystems. Details on the specifications can also be found in (Del Corso et al., 2007; Passerone et al, 2008). The second part, indeed exploits the experience achieved during the PiCPoT development and describes a proposal for a low-cost modular architecture for satellite

An ART1 microchip and its use in multi-ART1 systems

Recently, a real-time clustering microchip neural engine based on the ART1 architecture has been reported. Such chip is able to cluster 100-b patterns into up to 18 categories at a speed of 1.8 μs per pattern. However, that chip rendered an extremely high silicon area consumption of 1 cm2, and consequently an extremely low yield of 6%. Redundant circuit techniques can be introduced to improve yield performance at the cost of further increasing chip size. In this paper we present an improved ART1 chip prototype based on a different approach to implement the most area consuming circuit elements of the first prototype: an array of several thousand current sources which have to match within a precision of around 1%. Such achievement was possible after a careful transistor mismatch characterization of the fabrication process (ES2-1.0 μm CMOS). A new prototype chip has been fabricated which can cluster 50-b input patterns into up to ten categories. The chip has 15 times less area, shows a yield performance of 98%, and presents the same precision and speed than the previous prototype. Due to its higher robustness multichip systems are easily assembled. As a demonstration we show results of a two-chip ART1 system, and of an ARTMAP system made of two ART1 chips and an extra interfacing chip

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

REAL-TIME ERROR DETECTION AND CORRECTION FOR ROBUST OPERATION OF AUTONOMOUS SYSTEMS USING ENCODED STATE CHECKS

The objective of the proposed research is to develop methodologies, support algorithms and software-hardware infrastructure for detection, diagnosis, and correction of failures for actuators, sensors and control software in linear and nonlinear state variable systems with the help of multiple checks employed in the system. This objective is motivated by the proliferation of autonomous sense-and-control real-time systems, such as intelligent robots and self-driven cars which must maintain a minimum level of performance in the presence of electro-mechanical degradation of system-level components in the field as well as external attacks in the form of transient errors. A key focus is on rapid recovery from the effects of such anomalies and impairments with minimal impact on system performance while maintaining low implementation overhead as opposed to traditional schemes for recovery that rely on duplication or triplication. On-line detection, diagnosis and correction techniques are investigated and rely on analysis of system under test response signatures to real-time stimulus. For on-line error detection and diagnosis, linear and nonlinear state space encodings of the system under test are used and specific properties of the codes, as well as machine learning model based approaches were used are analyzed in real-time. Recovery is initiated by copying check model values to correct error for sensor and control software malfunction, and by redesigning the controller parameter on-the-fly for actuators to restore system performance. Future challenges that need to be addressed include viability studies of the proposed techniques on mobile autonomous system in distributed setting as well as application to systems with soft as well as hard real-time performance constraints.Ph.D

Measurements, Models, Systems and Design

531 s.

Enabling high-performance, mixed-signal approximate computing

textFor decades, the semiconductor industry enjoyed exponential improvements in microprocessor power and performance with the device scaling of successive technology generations. Scaling limitations at sub-micron technologies, however, have ceased to provide these historical performance improvements within a limited power budget. While device scaling provides a larger number of transistors per chip, for the same chip area, a growing percentage of the chip will have to be powered off at any given time due to power constraints. As such, the architecture community has focused on energy-efficient designs and is looking to specialized hardware to provide gains in performance. A focus on energy efficiency, along with increasingly less reliable transistors due to device scaling, has led to research in the area of approximate computing, where accuracy is traded for energy efficiency when precise computation is not required. There is a growing body of approximation-tolerant applications that, for example, compute on noisy or incomplete data, such as real-world sensor inputs, or make approximations to decrease the computation load in the analysis of cumbersome data sets. These approximation-tolerant applications span application domains, such as machine learning, image processing, robotics, and financial analysis, among others. Since the advent of the modern processor, computing models have largely presumed the attribute of accuracy. A willingness to relax accuracy requirements, however, with goal of gaining energy efficiency, warrants the re-investigation of the potential of analog computing. Analog hardware offers the opportunity for fast and low-power computation; however, it presents challenges in the form of accuracy. Where analog compute blocks have been applied to solve fixed-function problems, general-purpose computing has relied on digital hardware implementations that provide generality and programmability. The work presented in this thesis aims to answer the following questions: Can analog circuits be successfully integrated into general-purpose computing to provide performance and energy savings? And, what is required to address the historical analog challenges of inaccuracy, programmability, and a lack of generality to enable such an approach? This thesis work investigates a neural approach as a means to address the historical analog challenges of inaccuracy, programmability, and generality and to enable the use of analog circuits in general-purpose, high-performance computing. The first piece of this thesis work investigates the use of analog circuits at the microarchitecture level in the form of an analog neural branch predictor. The task of branch prediction can tolerate imprecision, as roll-back mechanisms correct for branch mispredictions, and application-level accuracy remains unaffected. We show that analog circuits enable the implementation of a highly-accurate, neural-prediction algorithm that is infeasible to implement in the digital domain. The second piece of this thesis work presents a neural accelerator that targets approximation-tolerant code. Analog neural acceleration provides application speedup of 3.3x and energy savings of 12.1x with a quality loss less than 10% for all except one approximation-tolerant benchmark. These results show that, using a neural approach, analog circuits can be applied to provide performance and energy efficiency in high-performance, general-purpose computing.Computer Science