37,136 research outputs found
An Overview of Starfish: A Table-Centric Tool for Interactive Synthesis
Engineering is an interactive process that requires intelligent interaction at many levels. My thesis [1] advances an engineering discipline for high-level synthesis and architectural decomposition that integrates perspicuous representation, designer interaction, and mathematical rigor. Starfish, the software prototype for the design method, implements a table-centric transformation system for reorganizing control-dominated system expressions into high-level architectures. Based on the digital design derivation (DDD) system a designer-guided synthesis technique that applies correctness preserving transformations to synchronous data flow specifications expressed as co- recursive stream equations Starfish enhances user interaction and extends the reachable design space by incorporating four innovations: behavior tables, serialization tables, data refinement, and operator retiming. Behavior tables express systems of co-recursive stream equations as a table of guarded signal updates. Developers and users of the DDD system used manually constructed behavior tables to help them decide which transformations to apply and how to specify them. These design exercises produced several formally constructed hardware implementations: the FM9001 microprocessor, an SECD machine for evaluating LISP, and the SchemEngine, garbage collected machine for interpreting a byte-code representation of compiled Scheme programs. Bose and Tuna, two of DDD s developers, have subsequently commercialized the design derivation methodology at Derivation Systems, Inc. (DSI). DSI has formally derived and validated PCI bus interfaces and a Java byte-code processor; they further executed a contract to prototype SPIDER-NASA's ultra-reliable communications bus. To date, most derivations from DDD and DRS have targeted hardware due to its synchronous design paradigm. However, Starfish expressions are independent of the synchronization mechanism; there is no commitment to hardware or globally broadcast clocks. Though software back-ends for design derivation are limited to the DDD stream-interpreter, targeting synchronous or real-time software is not substantively different from targeting hardware
Deductive Optimization of Relational Data Storage
Optimizing the physical data storage and retrieval of data are two key
database management problems. In this paper, we propose a language that can
express a wide range of physical database layouts, going well beyond the row-
and column-based methods that are widely used in database management systems.
We use deductive synthesis to turn a high-level relational representation of a
database query into a highly optimized low-level implementation which operates
on a specialized layout of the dataset. We build a compiler for this language
and conduct experiments using a popular database benchmark, which shows that
the performance of these specialized queries is competitive with a
state-of-the-art in memory compiled database system
LOT: Logic Optimization with Testability - new transformations for logic synthesis
A new approach to optimize multilevel logic circuits is introduced. Given a multilevel circuit, the synthesis method optimizes its area while simultaneously enhancing its random pattern testability. The method is based on structural transformations at the gate level. New transformations involving EX-OR gates as well as ReedâMuller expansions have been introduced in the synthesis of multilevel circuits. This method is augmented with transformations that specifically enhance random-pattern testability while reducing the area. Testability enhancement is an integral part of our synthesis methodology. Experimental results show that the proposed methodology not only can achieve lower area than other similar tools, but that it achieves better testability compared to available testability enhancement tools such as tstfx. Specifically for ISCAS-85 benchmark circuits, it was observed that EX-OR gate-based transformations successfully contributed toward generating smaller circuits compared to other state-of-the-art logic optimization tools
H-Infinity Optimal Interconnections
In this paper, a general ââ problem for continuous time, linear time invariant systems is formulated and solved in a behavioral framework. This general formulation, which includes standard ââ optimization as a special case, provides added freedom in the design of sub-optimal compensators, and can in fact be viewed as a means of designing optimal systems. In particular, the formulation presented allows for singular interconnections, which naturally occur when interconnecting first principles models
Calipso: Physics-based Image and Video Editing through CAD Model Proxies
We present Calipso, an interactive method for editing images and videos in a
physically-coherent manner. Our main idea is to realize physics-based
manipulations by running a full physics simulation on proxy geometries given by
non-rigidly aligned CAD models. Running these simulations allows us to apply
new, unseen forces to move or deform selected objects, change physical
parameters such as mass or elasticity, or even add entire new objects that
interact with the rest of the underlying scene. In Calipso, the user makes
edits directly in 3D; these edits are processed by the simulation and then
transfered to the target 2D content using shape-to-image correspondences in a
photo-realistic rendering process. To align the CAD models, we introduce an
efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
non-rigid alignment while preserving the high-level structure of the input
shape. Moreover, the user can choose to exploit image flow to estimate scene
motion, producing coherent physical behavior with ambient dynamics. We
demonstrate Calipso's physics-based editing on a wide range of examples
producing myriad physical behavior while preserving geometric and visual
consistency.Comment: 11 page
Tailored Source Code Transformations to Synthesize Computationally Diverse Program Variants
The predictability of program execution provides attackers a rich source of
knowledge who can exploit it to spy or remotely control the program. Moving
target defense addresses this issue by constantly switching between many
diverse variants of a program, which reduces the certainty that an attacker can
have about the program execution. The effectiveness of this approach relies on
the availability of a large number of software variants that exhibit different
executions. However, current approaches rely on the natural diversity provided
by off-the-shelf components, which is very limited. In this paper, we explore
the automatic synthesis of large sets of program variants, called sosies.
Sosies provide the same expected functionality as the original program, while
exhibiting different executions. They are said to be computationally diverse.
This work addresses two objectives: comparing different transformations for
increasing the likelihood of sosie synthesis (densifying the search space for
sosies); demonstrating computation diversity in synthesized sosies. We
synthesized 30184 sosies in total, for 9 large, real-world, open source
applications. For all these programs we identified one type of program analysis
that systematically increases the density of sosies; we measured computation
diversity for sosies of 3 programs and found diversity in method calls or data
in more than 40% of sosies. This is a step towards controlled massive
unpredictability of software
- âŠ