A robust window-based multi-node minimization technique using Boolean relations

Multi-node optimization using Boolean relations is a powerful approach for network minimization. The approach has been studied in theory, and so far its superiority over single node optimization techniques has only been conjectured for practical designs. This is due to the highly memory intensive computations involved in the calculation of Boolean relations representing the multi-node optimization exibility. In this thesis, an algorithm to perform Boolean relation-based multi-node optimization using a robust, fast and memory efcient algorithm is presented. In particular, two nodes are simultaneously optimized at a time. Results are reported on large designs, demonstrating the initial power of this multi-node optimization algorithm. The robustness of the approach arises from the use of a window-based technique for computing these Boolean relations. Secondly, aggressive early quantication is performed during the computation, keeping memory utilization low. Finally, smart heuristics are employed for selecting the node pair to be optimized simultaneously. These features allow the approach to scale well and provide good results for large designs. Experiments are performed on a set of large benchmarks and the algorithm's performance is compared to a SAT-based network optimization technique using complete don't cares. On average, the approach presented in this thesis achieves a 12% reduction in literal count across all the large designs compared to the complete don't cares, while maintaining small runtimes and low memory usage

A New Method to Express Functional Permissibilities for LUT based FPGAs and Its Applications

This paper presents a new method to express functional permissibilities for look-up table (LUT) based field programmable gate arrays (FPGAs). The method represents functional permissibilities by using sets of pairs of functions, not by incompletely specified functions. It makes good use of the properties of LUTs such that their internal logics can be freely changed. The permissibilities expressed by the proposed method have the desired property that at many points of a network they can be simultaneously treated. Applications of the proposed method are also presented; a method to optimize networks and a method to remove connections that are obstacles at the routing step. Preliminary experimental results are given to show the effectiveness of our proposed method. 1 Introduction Because of their low cost, re-programmability and rapid turnaround times, field programmable gate arrays (FPGAs) have emerged as an attractive means to implement low volume applications and prototypes[1]. FPGAs ..

Application of Logic Synthesis Toward the Inference and Control of Gene Regulatory Networks

In the quest to understand cell behavior and cure genetic diseases such as cancer, the fundamental approach being taken is undergoing a gradual change. It is becoming more acceptable to view these diseases as an engineering problem, and systems engineering approaches are being deployed to tackle genetic diseases. In this light, we believe that logic synthesis techniques can play a very important role. Several techniques from the field of logic synthesis can be adapted to assist in the arguably huge effort of modeling cell behavior, inferring biological networks, and controlling genetic diseases. Genes interact with other genes in a Gene Regulatory Network (GRN) and can be modeled as a Boolean Network (BN) or equivalently as a Finite State Machine (FSM). As the expression of genes deter- mine cell behavior, important problems include (i) inferring the GRN from observed gene expression data from biological measurements, and (ii) using the inferred GRN to explain how genetic diseases occur and determine the ”best” therapy towards treatment of disease. We report results on the application of logic synthesis techniques that we have developed to address both these problems. In the first technique, we present Boolean Satisfiability (SAT) based approaches to infer the predictor (logical support) of each gene that regulates melanoma, using gene expression data from patients who are suffering from the disease. From the output of such a tool, biologists can construct targeted experiments to understand the logic functions that regulate a particular target gene. Our second technique builds upon the first, in which we use a logic synthesis technique; implemented using SAT, to determine gene regulating functions for predictors and gene expression data. This technique determines a BN (or family of BNs) to describe the GRN and is validated on a synthetic network and the p53 network. The first two techniques assume binary valued gene expression data. In the third technique, we utilize continuous (analog) expression data, and present an algorithm to infer and rank predictors using modified Zhegalkin polynomials. We demonstrate our method to rank predictors for genes in the mutated mammalian and melanoma networks. The final technique assumes that the GRN is known, and uses weighted partial Max-SAT (WPMS) towards cancer therapy. In this technique, the GRN is assumed to be known. Cancer is modeled using a stuck-at fault model, and ATPG techniques are used to characterize genes leading to cancer and select drugs to treat cancer. To steer the GRN state towards a desirable healthy state, the optimal selection of drugs is formulated using WPMS. Our techniques can be used to find a set of drugs with the least side-effects, and is demonstrated in the context of growth factor pathways for colon cancer