Area- Efficient VLSI Implementation of Serial-In Parallel-Out Multiplier Using Polynomial Representation in Finite Field GF(2m)

Finite field multiplier is mainly used in elliptic curve cryptography, error-correcting codes and signal processing. Finite field multiplier is regarded as the bottleneck arithmetic unit for such applications and it is the most complicated operation over finite field GF(2m) which requires a huge amount of logic resources. In this paper, a new modified serial-in parallel-out multiplication algorithm with interleaved modular reduction is suggested. The proposed method offers efficient area architecture as compared to proposed algorithms in the literature. The reduced finite field multiplier complexity is achieved by means of utilizing logic NAND gate in a particular architecture. The efficiency of the proposed architecture is evaluated based on criteria such as time (latency, critical path) and space (gate-latch number) complexity. A detailed comparative analysis indicates that, the proposed finite field multiplier based on logic NAND gate outperforms previously known resultsComment: 19 pages, 4 figure

NASA Space Engineering Research Center Symposium on VLSI Design

The NASA Space Engineering Research Center (SERC) is proud to offer, at its second symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories and the electronics industry. These featured speakers share insights into next generation advances that will serve as a basis for future VLSI design. Questions of reliability in the space environment along with new directions in CAD and design are addressed by the featured speakers

Integrative computational methodologies on single cell datasets

High throughput single cell sequencing has seen exciting developments in recent years. With its high resolution characterization of genetics, genomics, proteomics, and epigenomics features, single cell data offer more insights on the underlying biological processes than those from bulk sequencing data. The most well developed single cell technologies are single cell RNA-seq (scRNA-seq) on transcriptomics and flow cytometry on proteomics. Many multi-omics single cell sequencing platforms have also emerged recently, such as CITE-seq, which profiles both epitope and transcriptome simultaneously. But some well known limitations of single cell data, such as batch variations, shallow sequencing depth, and sparsity also present many challenges. Many computational approaches built on machine learning and deep learning methods have been proposed to address these challenges. In this dissertation, I present three computational methods for joint analysis of single cell sequencing data either by multi-omics integration or joint analysis of multiple datasets. In the first chapter, we focus on single cell proteomics data, specifically, the antibody profiling of CITE-seq and cytometry by time of flight (CyTOF) applied to single cells to measure surface marker abundance. Although CyTOF has high accuracy and was introduced earlier than scRNA-seq, there is a lack of computational methods on cell type classification and annotations for these data. We propose a novel automated cell type annotation tool by incorporating CITE-seq data from the same tissue, publicly available annotated scRNA-seq data, and prior knowledge of surface markers in the literature. Our new method, called automated single cell proteomics data annotation approach (ProtAnno), is based on non-negative matrix factorization. We demonstrate the annotation accuracy and robustness of ProtAnno through extensive applications, especially for peripheral blood mononuclear cells (PBMC). The second chapter introduces an integrative method improving bulk sequencing data decomposition into cell type proportions by harmonizing scRNA-seq data across multiple tissues or multiple studies. As a Bayesian model, our method, called tranSig, is able to construct a more reliable signature matrix for decomposition by borrowing information from other tissues and/or studies. Our method can be considered an add-on step in cell type decomposition. Our method can better derive signature gene matrix and better characterize the biological heterogeneity from bulk sequencing datasets. Finally, in the last chapter, we propose a method to jointly analyze scRNA-seq data with summary statistics from genome wide association studies (GWAS). Our method generates a set of SNP (single nucelotide polymorphism)-level weight scores for each cell type or tissue type using scRNA-seq atlas. These scores are combined with risk allele effect sizes to decompose polygenic risk score (PRS) into cell types or tissue types. We show through enrichment analysis and phenome-wide association study (PheWAS) that the decomposed PRSs can better explain the biological mechanisms of genetic effects on complex traits mediated through transcription regulation and the differences across cell types and tissues

The 1982 NASA/ASEE Summer Faculty Fellowship Program

A NASA/ASEE Summer Faculty Fellowship Research Program was conducted to further the professional knowledge of qualified engineering and science faculty members, to stimulate an exchange of ideas between participants and NASA, to enrich and refresh the research and teaching activities of participants' institutions, and to contribute to the research objectives of the NASA Centers

Simulation and implementation of novel deep learning hardware architectures for resource constrained devices

Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems