Statistical Learning in Chip (SLIC) (Invited Paper)

Abstract-Despite best efforts, integrated systems are "born" (manufactured) with a unique 'personality' that stems from our inability to precisely fabricate their underlying circuits, and create software a priori for controlling the resulting uncertainty. It is possible to use sophisticated test methods to identify the bestperforming systems but this would result in unacceptable yields and correspondingly high costs. The system personality is further shaped by its environment (e.g., temperature, noise and supply voltage) and usage (i.e., the frequency and type of applications executed), and since both can fluctuate over time, so can the system's personality. Systems also "grow old" and degrade due to various wear-out mechanisms (e.g., negative-bias temperature instability), and unexpectedly due to various early-life failure sources. These "nature and nurture" influences make it extremely difficult to design a system that will operate optimally for all possible personalities. To address this challenge, we propose to develop statistical learning in-chip (SLIC). SLIC is a holistic approach to integrated system design based on continuously learning key personality traits on-line, for selfevolving a system to a state that optimizes performance hierarchically across the circuit, platform, and application levels. SLIC will not only optimize integrated-system performance but also reduce costs through yield enhancement since systems that would have before been deemed to have weak personalities (unreliable, faulty, etc.) can now be recovered through the use of SLIC

Genome-wide analysis of heterogeneous nuclear ribonucleoprotein (hnRNP) binding to HIV-1 RNA reveals a key role for hnRNP H1 in alternative viral mRNA splicing

Alternative splicing of HIV-1 mRNAs increases viral coding potential and controls the levels and timing of gene expression. HIV-1 splicing is regulated in part by heterogeneous nuclear ribonucleoproteins (hnRNPs) and their viral target sequences, which typically repress splicing when studied outside their native viral context. Here, we determined the location and extent of hnRNP binding to HIV-1 mRNAs and their impact on splicing in a native viral context. Notably, hnRNP A1, hnRNP A2, and hnRNP B1 bound to many dispersed sites across viral mRNAs. Conversely, hnRNP H1 bound to a few discrete purine-rich sequences, a finding that was mirrore

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

Novel genetic loci underlying human intracranial volume identified through genome-wide association

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five novel loci for intracranial volume and confirmed two known signals. Four of the loci are also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic=0.748), which indicated a similar genetic background and allowed for the identification of four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, Parkinson’s disease, and enriched near genes involved in growth pathways including PI3K–AKT signaling. These findings identify biological underpinnings of intracranial volume and provide genetic support for theories on brain reserve and brain overgrowth

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG

False coupling interactions in static timing analysis

Neighboring line switching can contribute to a large portion of the delay of a line for today’s deep submicron designs. In order to avoid excessive conservatism in static timing analysis, it is important to determine if aggressor lines can potentially switch simultaneously with the victim. In this paper, we present a comprehensive ATPG-based approach that uses functional information to identify valid interactions between coupled lines. Our algorithm accounts for glitches on aggressors that can be caused by static and dynamic hazards in the circuit. We present results on several benchmark circuits that show the value of considering functional information to reduce the conservatism associated with worst-case coupled line switching assumptions during static timing analysis