Fuzzy memoization for floating-point multimedia applications

Instruction memoization is a promising technique to reduce the power consumption and increase the performance of future low-end/mobile multimedia systems. Power and performance efficiency can be improved by reusing instances of an already executed operation. Unfortunately, this technique may not always be worth the effort due to the power consumption and area impact of the tables required to leverage an adequate level of reuse. In this paper, we introduce and evaluate a novel way of understanding multimedia floating-point operations based on the fuzzy computation paradigm: performance and power consumption can be improved at the cost of small precision losses in computation. By exploiting this implicit characteristic of multimedia applications, we propose a new technique called tolerant memoization. This technique expands the capabilities of classic memoization by associating entries with similar inputs to the same output. We evaluate this new technique by measuring the effect of tolerant memoization for floating-point operations in a low-power multimedia processor and discuss the trade-offs between performance and quality of the media outputs. We report energy improvements of 12 percent for a set of key multimedia applications with small LUT of 6 Kbytes, compared to 3 percent obtained using previously proposed techniques.Peer ReviewedPostprint (published version

Software Reuse across Robotic Platforms: Limiting the effects of diversity

Robots have diverse capabilities and complex interactions with their environment. Software development for robotic platforms is time consuming due to the complex nature of the tasks to be performed. Such an environment demands sound software engineering practices to produce high quality software. However software engineering in the robotics domain fails to facilitate any significant level of software reuse or portability. This paper identifies the major issues limiting software reuse in the robotics domain. Lack of standardisation, diversity of robotic platforms, and the subtle effects of environmental interaction all contribute to this problem. It is then shown that software components, fuzzy logic, and related techniques can be used together to address this problem. While complete software reuse is not possible, it is demonstrated that significant levels of software reuse can be obtained. Without an acceptable level of reuse or portability, software engineering in the robotics domain will not be able to meet the demands of a rapidly developing field. The work presented in this paper demonstrates a method for supporting software reuse across robotic platforms and hence facilitating improved software engineering practices

Neuron-level fuzzy memoization in RNNs

The final publication is available at ACM via http://dx.doi.org/10.1145/3352460.3358309Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the accuracy of future predictions and, therefore, they are very effective for sequence processing problems. For each application run, each recurrent layer is executed many times for processing a potentially large sequence of inputs (words, images, audio frames, etc.). In this paper, we make the observation that the output of a neuron exhibits small changes in consecutive invocations. We exploit this property to build a neuron-level fuzzy memoization scheme, which dynamically caches the output of each neuron and reuses it whenever it is predicted that the current output will be similar to a previously computed result, avoiding in this way the output computations. The main challenge in this scheme is determining whether the new neuron's output for the current input in the sequence will be similar to a recently computed result. To this end, we extend the recurrent layer with a much simpler Bitwise Neural Network (BNN), and show that the BNN and RNN outputs are highly correlated: if two BNN outputs are very similar, the corresponding outputs in the original RNN layer are likely to exhibit negligible changes. The BNN provides a low-cost and effective mechanism for deciding when fuzzy memoization can be applied with a small impact on accuracy. We evaluate our memoization scheme on top of a state-of-the-art accelerator for RNNs, for a variety of different neural networks from multiple application domains. We show that our technique avoids more than 24.2% of computations, resulting in 18.5% energy savings and 1.35x speedup on average.Peer ReviewedPostprint (author's final draft

Reusable Software Components for Robots Using Fuzzy Abstractions

Mobile robots today, while varying greatly in design, often have a large number of similarities in terms of their tasks and goals. Navigation, obstacle avoidance, and vision are all examples. In turn, robots of similar design, but with varying configurations, should be able to share the bulk of their controlling software. Any changes required should be minimal and ideally only to specify new hardware configurations. However, it is difficult to achieve such flexibility, mainly due to the enormous variety of robot hardware available and the huge number of possible configurations. Monolithic controllers that can handle such variety are impossible to build. This paper will investigate these portability problems, as well as techniques to manage common abstractions for user-designed components. The challenge is in creating new methods for robot software to support a diverse variety of robots, while also being easily upgraded and extended. These methods can then provide new ways to support the operational and functional reuse of the same high-level components across a variety of robots

Towards product platform introduction: optimising commonality of components

Companies that design and manufacture products for a wide range of related applications need to offer the right product for each use. A platform design strategy allows designing the product range based on product platforms, where some of the components and systems are common across the range whereas other components are individual for each product variant. This paper presents the problems that a company faces when trying to introduce a platform strategy and outlines a method to find suitable components to be made common. The method is shown with a simple case. The approach uses fuzzy logic to obtain a suitable criterion to assess the overall value of the product line and a genetic algorithm for finding the set of components to be made commo

Automatic allocation of safety requirements to components of a software product line

Safety critical systems developed as part of a product line must still comply with safety standards. Standards use the concept of Safety Integrity Levels (SILs) to drive the assignment of system safety requirements to components of a system under design. However, for a Software Product Line (SPL), the safety requirements that need to be allocated to a component may vary in different products. Variation in design can indeed change the possible hazards incurred in each product, their causes, and can alter the safety requirements placed on individual components in different SPL products. Establishing common SILs for components of a large scale SPL by considering all possible usage scenarios, is desirable for economies of scale, but it also poses challenges to the safety engineering process. In this paper, we propose a method for automatic allocation of SILs to components of a product line. The approach is applied to a Hybrid Braking System SPL design

A metric to represent the evolution of CAD/analysis models in collaborative design

Computer Aided Design (CAD) and Computer Aided Engineering (CAE) models are often used during product design. Various interactions between the different models must be managed for the designed system to be robust and in accordance with initially defined specifications. Research published to date has for example considered the link between digital mock-up and analysis models. However design/analysis integration must take into consideration the important number of models (digital mock-up and simulation) due to model evolution in time, as well as considering system engineering. To effectively manage modifications made to the system, the dependencies between the different models must be known and the nature of the modification must be characterised to estimate the impact of the modification throughout the dependent models. We propose a technique to describe the nature of a modification which may be used to determine the consequence within other models as well as a way to qualify the modified information. To achieve this, a metric is proposed that allows the qualification and evaluation of data or information, based on the maturity and validity of information and model

Designing Software Architectures As a Composition of Specializations of Knowledge Domains

This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: ‘A software architecture should be a composition of specializations of knowledge domains’. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience

A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning

Real world combinatorial optimization problems such as scheduling are typically too complex to solve with exact methods. Additionally, the problems often have to observe vaguely specified constraints of different importance, the available data may be uncertain, and compromises between antagonistic criteria may be necessary. We present a combination of approximate reasoning based constraints and iterative optimization based heuristics that help to model and solve such problems in a framework of C++ software libraries called StarFLIP++. While initially developed to schedule continuous caster units in steel plants, we present in this paper results from reusing the library components in a shift scheduling system for the workforce of an industrial production plant.Comment: 33 pages, 9 figures; for a project overview see http://www.dbai.tuwien.ac.at/proj/StarFLIP