20226862
Foundations for a Formalism of Nearness
Reasoning about your spatial environment can be a challenging task, especially when you are a robot trying to rely solely on quantitative measures. With nearness being such a vague concept, a qualitative representation is an obvious choice offering a wider range of possible values.This paper introduces a qualitative representation for spatial proximity that accounts for absolute binary nearness relations. The formalism is based on the notion of perceived points, called sites, in a point based universe. Proximity concepts are determined by the parameters of distance between two sites and weight of each of those sites. These parameters were drawn from the concept of Generalised Voronoi Diagrams.Cognitively useful models and interpretations of our formalism are shown in both a navigation and a natural language context.
39139980
On Sufficient Conditions for Learnability of Logic Programs from Positive Data
Shinohara, Arimura, and Krishna Rao have shown learnability in the limit of minimal models of classes of logic programs from positive only data. In most cases, these results involve logic programs in which the "size" of the head yields a bound on the size of the body literals. However, when local variables are present, such a bound on body literal size cannot directly be ensured. The above authors achieve such a restriction using technical notions like mode and linear inequalities. The present paper develops a conceptually clean framework where the behavior of local variables is controlled by nonlocal ones. It is shown that for certain classes of logic programs, learnablity from positive data is equivalent to limiting identification of bounds for the number of clauses and the number of local variables. This reduces the learning problem finding two integers. This cleaner framework generalizes all the known results and establishes learnability of new classes.
7592429
On the Convergence of Incremental Knowledge Base Construction
Ripple Down Rules is a practical methodology to build knowledge-based systems, which has proved successful in a wide range of commercial applications. However, little work has been done on its theoretical foundations. In this paper, we formalise the key features of the method. We present the process of building a correct knowledge base as a discovery scenario involving a user, an expert, and a system. The user provides data for classification. The expert helps the system to build its knowledge base incrementally, using the output of the latter in response to the last datum provided by the user. In case the system’s output is not satisfactory, the expert guides the system to improve its future performance while not affecting its ability to properly classify past data. We examine under which conditions the sequence of knowledge bases constructed by the system eventually converges to a knowledge base that faithfully represents the target classification function. Our results are in accordance with the observed behaviour of real-life systems.
37212285
Quantification over names and modalities
38643151
Learning from Each Other
Since its inception, the field of machine learning has seen the advent of several learning paradigms, designed to frame the issues central to the learning activity, provide effective learning methods, and investigate the power and limitations inherent to the process of successful learning. In this article, we propose a formalization that underlies the key concepts of many such paradigms and discuss their relevance to scientific discovery, with the aim of assessing what scientists can expect from machines designed to assist them in their quest for the discovery of valid laws. We illustrate the formalization on several variations of a card game, and highlight the differences that paradigms impose on learners, as well as the assumptions they make on the nature of the learning process. We then use the formalization to describe a multi-agent interaction protocol, that has been inspired by these paradigms and that has been validated recently on some groups of agents. Finally, we propose extensions to this protocol.
2402100
Input-Dependence in Function-Learning
In the standard literature on inductive inference, a learner sees as input the course of values of the function to be learned. In the present work, it is investigated how reasonable this choice is and how sensitive the model is with respect to variations like the overgraph or undergraph of the function. Several implications and separations are shown and for the basic notions, a complete picture is obtained. Furthermore, relations to oracles, additional information and teams are explored.
16535802
Power Consumption Modeling and Characterization of the TI C6201
This new approach characterizes power dissipation on complex dsps. its processor model relies on an initial functional-level power analysis of the target processor together with a characterization that qualifies the more significant architectural and algorithmic parameters for power dissipation. these parameters come from a simple profiling of the assembly code. This functional model accounts for deeply pipelined, superscalar, and hierarchical memory architectures.
15088597
G729 Voice Decoder Design
Embedded digital signal processing (DSP) systems are usually associated with real time constraints and/or high data rates such that fully software implementations are often not satisfactory. In that case, mixed hardware/software implementations are to be investigated. This paper presents the design of a HW/SW G.729 voice decoder dedicated to embedded systems. The decoder has been built around, on the one hand a reconfigurable digital circuit (FPGA) to achieve the so called IP hardware part—the autocorrelation computation—using a linear systolic array, and on the other hand a digital signal processor (DSP) for the remainder of the algorithm. Apart such an implementation is typically driven by the use of reusable component (IP) it is of great interest for new G729-based applications such as Voice over IP (VoIP) for example. It results in an overall reduction of the execution time per frame. Another interesting point is the design of a parameterizable autocorrelation block which can be useful for a wide range of applications such as GSM 13 Kbit/s, APC 9.6 Kbit/s and G723 6.3 Kbit/s and 5.3 Kbit/s. In the G729 context and using a V50 Virtex FPGA, the execution time of this function is 10 times faster than a TMS320C6201 DSP implementation.
7274427
Low Power Synthesis Methodology with Data Format Optimization Applied on a DWT
In modern VLSI circuits, power consumption has become a design criteria as well as speed and silicon area or gate count. To guide the designer implementing complex applications with real time constraint on dedicated circuits, we present a High Level Synthesis methodology that provides a Register Transfer Level description of an ASIC, from a behavioral description of an algorithm; this complete methodology uses basic techniques such as selection, assignment or scheduling specified for power optimization, associated with a new approach based on data format optimization. Actually, instead of the usual 32-bit floating-point format, it is power efficient to use a fixed-point format provided overflow and computation noise problems are solved. The application of our method on an usual DSP example (the DWT) shows a power reduction of more than 60%.
11400223
High-level synthesis under I/O timing and memory constraints
In the design of complex systems-on-chips, it is necessary to take into account communication and memory access constraints for the integration of a dedicated hardware accelerator. We present a methodology and a tool that allow the high-level synthesis of a DSP algorithm, under both I/O timing and memory constraints. Based on formal models and a generic architecture, this tool helps the designer to find a reasonable trade-off between the required I/O timing behavior and the internal memory access parallelism of the circuit. The interest of our approach is demonstrated by the case study of an FFT algorithm.
11623202
A memory aware behavioral synthesis tool for real-time VLSI circuits
We introduce a new approach to take into account the memory architecture and the memory mapping in the Behavioral Synthesis of Real-Time VLSI circuits. We formalize the memory mapping as a set of constraints for the thesis, and defined a Memory Constraint Graph and an accessibility criterion to be used in the scheduling step. We use a memory mapping file to include those memory straints in our HLS tool GAUT. Our scheduling algorithm exhibits a relatively low complexity that permits to tackle complex designs in a reasonable time. Several experiments are performed to demonstrate the efficiency of our method, and to compare GAUT with an industrial behavioral synthesis tool. We finally show how to explore, with the help of GAUT, a wide range of solutions, and to reach a good tradeoffs between time, power-consumption, and area.