Abstracts for A. Richard Newton

The EECS Research Summary for 2003

A Practical and Adaptive Multi-Stroke Symbol Recognition System

Heloise Hse
(Professor A. Richard Newton)

With the increasing popularity and availability of devices like personal digital assistants (PDAs), pen computing is becoming more familiar to end users. However, many applications are still designed with moded windows, icons, menus, and a pointer (WIMP) interfaces that treat pens like pointing devices instead of using them for what they are good for: sketching. We are interested in providing a solid infrastructure and a set of utilities for developing sketch-based user interfaces for a variety of applications.

Sketching is simple and natural and is especially desirable for conceptual design either on an individual basis or in a collaborative environment. By embedding recognition engines in sketch-based programs, the resulting drawings can be interpreted and processed. Various computations can be applied to recognized sketches, therefore fully leveraging the power of electronic design tools while maintaining the ease of sketching.

Currently, we are working on multi-stroke symbol recognition for a class of shapes that are commonly used in slide creation and diagram editing. Our technique is independent of stroke order, number, and direction, as well as invariant to rotation, scaling, translation, and reflection. We take the statistical approach using local features to capture shape information and relative position of strokes, thus utilizing both structural and statistical information learned from examples. Furthermore, the recognition system is adaptive such that it learns upon user correction. It also provides feedback to the user so that the user can better understand why misrecognition took place and can adapt the drawing style accordingly.

Send mail to the author : (hwawen@eecs.berkeley.edu)

Distributed Design Data Management for EDA

Mark D. Spiller
(Professor A. Richard Newton)
MARCO/DARPA Gigascale Silicon Research Center

System-on-a-chip designs will have a tremendous impact on the efficient management of design data. Time-to-market constraints require hierarchy and component re-use, while the increasing chip complexity, design sizes, and specialization of components will make geographically distributed design teams more likely. While in an ideal world the definition of interfaces would allow these distributed teams to work in parallel on their respective modules with little interaction, in reality a successful design is likely to require intensive interaction and collaboration throughout the design process. Critical to this process will be the ability to build, integrate, and test the distributed design components, which will require a scalable and efficient data caching architecture.

We are working to identify the needs and requirements for an architecture that will provide integration between heterogeneous tools and efficient support for collaborative incremental design. Areas of interest include relaxed transaction models suited towards collaborative work and intelligent, distributed caching of design data. We are combining tool usage metrics, design data characteristics, and likely interactions throughout the design process to build a representative model of EDA data interactions. This model will be used in experiments that will analyze the tradeoffs of different data management techniques in various design scenarios and resource organizations. Our research goal is to find reliable design data management and transactional semantics for large datasets in a distributed, potentially unreliable, network environment.

More information (http://www-cad.eecs.berkeley.edu/~mds) or

Send mail to the author : (mds@eecs.berkeley.edu)