ArchiBabel: Tracing the Writing Architecture Project in Architectural Education

Though much recent scholarship has investigated the potential of writing in creative practice (including visual arts, drama, even choreography), there are few models in the literature which discuss writing in the context of architectural education. The paper presented here aims to address this dearth of pedagogical research, analysing the cross-disciplinary Writing Architecture Project based in the undergraduate course of the School of Architecture at QUB. Over the course of four years, teaching staff, in partnership with the university's Learning Development Service, technicians and specialist librarians, have addressed an unfortunately persistent struggle for both architecture students and professionals alike to research and construct argument in written form. The paper examines the current problem as identified in the literature before analysing the efficacy of the variety of teaching methods used in the Writing Architecture Project, with conclusions about the project’s success and continuing challenges.

Design and Practice in Architectural Theory Education

Benefiting from design in theory learning is not common in architecture schools. The general practice is to design in studio and to theorise in lectures. In the undergraduate module History and Theory in Architecture II at Queen’s University Belfast, students attend interactive lectures, participate in reading group discussions, design TextObjects, and write essays. TextObjects contain textual, audio and/or graphic representations that highlight a single concept or a complex set of issues derived from readings. Students experiment with diverse media, such as filmmaking, photography, and graphic design, some of which they experience for the first time. Lectures and readings revolve around theories of architectural representation, media and communication, which are practiced through TextObjects. This is a new way to link theory and practice in architectural education. Through action research, this study analyses this innovative teaching method called TextObject, which brings design and practice into architectural theory education to stimulate students towards critical thinking. The pedagogical research of architectural theoretician Necdet Teymur (1992, 1996, 2002) underlies the study.

Architectural synthesis of an image processing algorithm using IRIS

Details are presented of the IRIS synthesis system for high-performance digital signal processing. This tool allows non-specialists to automatically derive VLSI circuit architectures from high-level, algorithmic representations, and provides a quick route to silicon implementation. The applicability of the system is demonstrated using the design example of a one-dimensional Discrete Cosine Transform circuit.

Changing Paradigms in Space Theories:Recapturing 20th Century Architectural History

The concept of space entered architectural history as late as 1893. Studies in art opened up the discussion, and it has been studied in various ways in architecture ever since. This article aims to instigate an additional reading to architectural history, one that is not supported by "isms" but based on space theories in the 20th century. Objectives of the article are to bring the concept of space and its changing paradigms to the attention of architectural researchers, to introduce a conceptual framework to classify and clarify theories of space, and to enrich the discussions on the 20th century architecture through theories that are beyond styles. The introduction of space in architecture will revolve around subject-object relationships, three-dimensionality and senses. Modern space will be discussed through concepts such as empathy, perception, abstraction, and geometry. A scientific approach will follow to study the concept of place through environment, event, behavior, and design methods. Finally, the research will look at contemporary approaches related to digitally supported space via concepts like reality-virtuality, mediated experience, and relationship with machines.

Efficiently exploring architectural design spaces via predictive modeling

Architects use cycle-by-cycle simulation to evaluate design choices and understand tradeoffs and interactions among design parameters. Efficiently exploring exponential-size design spaces with many interacting parameters remains an open problem: the sheer number of experiments renders detailed simulation intractable. We attack this problem via an automated approach that builds accurate, confident predictive design-space models. We simulate sampled points, using the results to teach our models the function describing relationships among design parameters. The models produce highly accurate performance estimates for other points in the space, can be queried to predict performance impacts of architectural changes, and are very fast compared to simulation, enabling efficient discovery of tradeoffs among parameters in different regions. We validate our approach via sensitivity studies on memory hierarchy and CPU design spaces: our models generally predict IPC with only 1-2% error and reduce required simulation by two orders of magnitude. We also show the efficacy of our technique for exploring chip multiprocessor (CMP) design spaces: when trained on a 1% sample drawn from a CMP design space with 250K points and up to 55x performance swings among different system configurations, our models predict performance with only 4-5% error on average. Our approach combines with techniques to reduce time per simulation, achieving net time savings of three-four orders of magnitude. Copyright © 2006 ACM.

Efficient Architectural Design Space Exploration via Predictive Modeling

Efficiently exploring exponential-size architectural design spaces with many interacting parameters remains an open problem: the sheer number of experiments required renders detailed simulation intractable.We attack this via an automated approach that builds accurate predictive models. We simulate sampled points, using results to teach our models the function describing relationships among design parameters. The models can be queried and are very fast, enabling efficient design tradeoff discovery. We validate our approach via two uniprocessor sensitivity studies, predicting IPC with only 1–2% error. In an experimental study using the approach, training on 1% of a 250-K-point CMP design space allows our models to predict performance with only 4–5% error. Our predictive modeling combines well with techniques that reduce the time taken by each simulation experiment, achieving net time savings of three-four orders of magnitude.