Logic shrinkage: learned connectivity sparsification for LUT-based neural networks

FPGA-specific DNN architectures using the native LUTs as independently trainable inference operators have been shown to achieve favorable area-accuracy and energy-accuracy tradeoffs. The first work in this area, LUTNet, exhibited state-of-the-art performance for standard DNN benchmarks. In this article, we propose the learned optimization of such LUT-based topologies, resulting in higher-efficiency designs than via the direct use of off-the-shelf, hand-designed networks. Existing implementations of this class of architecture require the manual specification of the number of inputs per LUT, K. Choosing appropriate K a priori is challenging, and doing so at even high granularity, e.g. per layer, is a time-consuming and error-prone process that leaves FPGAs’ spatial flexibility underexploited. Furthermore, prior works see LUT inputs connected randomly, which does not guarantee a good choice of network topology. To address these issues, we propose logic shrinkage, a fine-grained netlist pruning methodology enabling K to be automatically learned for every LUT in a neural network targeted for FPGA inference. By removing LUT inputs determined to be of low importance, our method increases the efficiency of the resultant accelerators. Our GPU-friendly solution to LUT input removal is capable of processing large topologies during their training with negligible slowdown. With logic shrinkage, we better the area and energy efficiency of the best-performing LUTNet implementation of the CNV network classifying CIFAR-10 by 1.54 × and 1.31 ×, respectively, while matching its accuracy. This implementation also reaches 2.71 × the area efficiency of an equally accurate, heavily pruned BNN. On ImageNet with the Bi-Real Net architecture, employment of logic shrinkage results in a post-synthesis area reduction of 2.67 × vs LUTNet, allowing for implementation that was previously impossible on today’s largest FPGAs. We validate the benefits of logic shrinkage in the context of real application deployment by implementing a face mask detection DNN using BNN, LUTNet and logic-shrunk layers. Our results show that logic shrinkage results in area gains versus LUTNet (up to 1.20 ×) and equally pruned BNNs (up to 1.08 ×), along with accuracy improvements

Polymorphisms in the Estrogen Receptor 1 and Vitamin C and Matrix Metalloproteinase Gene Families Are Associated with Susceptibility to Lymphoma

BACKGROUND: Non-Hodgkin lymphoma (NHL) is the fifth most common cancer in the U.S. and few causes have been identified. Genetic association studies may help identify environmental risk factors and enhance our understanding of disease mechanisms. METHODOLOGY/PRINCIPAL FINDINGS: 768 coding and haplotype tagging SNPs in 146 genes were examined using Illumina GoldenGate technology in a large population-based case-control study of NHL in the San Francisco Bay Area (1,292 cases 1,375 controls are included here). Statistical analyses were restricted to HIV- participants of white non-Hispanic origin. Genes involved in steroidogenesis, immune function, cell signaling, sunlight exposure, xenobiotic metabolism/oxidative stress, energy balance, and uptake and metabolism of cholesterol, folate and vitamin C were investigated. Sixteen SNPs in eight pathways and nine haplotypes were associated with NHL after correction for multiple testing at the adjusted q<0.10 level. Eight SNPs were tested in an independent case-control study of lymphoma in Germany (494 NHL cases and 494 matched controls). Novel associations with common variants in estrogen receptor 1 (ESR1) and in the vitamin C receptor and matrix metalloproteinase gene families were observed. Four ESR1 SNPs were associated with follicular lymphoma (FL) in the U.S. study, with rs3020314 remaining associated with reduced risk of FL after multiple testing adjustments [odds ratio (OR) = 0.42, 95% confidence interval (CI) = 0.23-0.77) and replication in the German study (OR = 0.24, 95% CI = 0.06-0.94). Several SNPs and haplotypes in the matrix metalloproteinase-3 (MMP3) and MMP9 genes and in the vitamin C receptor genes, solute carrier family 23 member 1 (SLC23A1) and SLC23A2, showed associations with NHL risk. CONCLUSIONS/SIGNIFICANCE: Our findings suggest a role for estrogen, vitamin C and matrix metalloproteinases in the pathogenesis of NHL that will require further validation

Leberhämangiome: Wie groß ist das Risiko einer Rupturblutung wirklich? Eine systematische Analyse der Weltliteratur von 1898 bis 2010

Saunders' Paratrooper - LP32 - photographed 1979