Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment

There has been growing interest in using photonic processors for performing neural network inference operations; however, these networks are currently trained using standard digital electronics. Here, we propose on-chip training of neural networks enabled by a CMOS-compatible silicon photonic architecture to harness the potential for massively parallel, efficient, and fast data operations. Our scheme employs the direct feedback alignment training algorithm, which trains neural networks using error feedback rather than error backpropagation, and can operate at speeds of trillions of multiply-accumulate (MAC) operations per second while consuming less than one picojoule per MAC operation. The photonic architecture exploits parallelized matrix-vector multiplications using arrays of microring resonators for processing multi-channel analog signals along single waveguide buses to calculate the gradient vector for each neural network layer in situ. We also experimentally demonstrate training deep neural networks with the MNIST dataset using on-chip MAC operation results. Our novel approach for efficient, ultra-fast neural network training showcases photonics as a promising platform for executing AI applications.Comment: 15 pages, 6 figure

Molecular Pathogenesis of EBV Susceptibility in XLP as Revealed by Analysis of Female Carriers with Heterozygous Expression of SAP

X-linked lymphoproliferative disease (XLP) is a primary immunodeficiency caused by mutations in SH2D1A which encodes SAP. SAP functions in signalling pathways elicited by the SLAM family of leukocyte receptors. A defining feature of XLP is exquisite sensitivity to infection with EBV, a B-lymphotropic virus, but not other viruses. Although previous studies have identified defects in lymphocytes from XLP patients, the unique role of SAP in controlling EBV infection remains unresolved. We describe a novel approach to this question using female XLP carriers who, due to random X-inactivation, contain both SAP+ and SAP− cells. This represents the human equivalent of a mixed bone marrow chimera in mice. While memory CD8+ T cells specific for CMV and influenza were distributed across SAP+ and SAP− populations, EBV-specific cells were exclusively SAP+. The preferential recruitment of SAP+ cells by EBV reflected the tropism of EBV for B cells, and the requirement for SAP expression in CD8+ T cells for them to respond to Ag-presentation by B cells, but not other cell types. The inability of SAP− clones to respond to Ag-presenting B cells was overcome by blocking the SLAM receptors NTB-A and 2B4, while ectopic expression of NTB-A on fibroblasts inhibited cytotoxicity of SAP− CD8+ T cells, thereby demonstrating that SLAM receptors acquire inhibitory function in the absence of SAP. The innovative XLP carrier model allowed us to unravel the mechanisms underlying the unique susceptibility of XLP patients to EBV infection in the absence of a relevant animal model. We found that this reflected the nature of the Ag-presenting cell, rather than EBV itself. Our data also identified a pathological signalling pathway that could be targeted to treat patients with severe EBV infection. This system may allow the study of other human diseases where heterozygous gene expression from random X-chromosome inactivation can be exploited

Role of Spatial Coherence in Diffractive Optical Neural Networks

Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. However, previous experimental demonstrations of DONNs have only been performed using coherent light, which is not present in the natural world. Here, we study the role of spatial optical coherence in DONN operation. We propose a numerical approach to efficiently simulate DONNs under input illumination with arbitrary spatial coherence and discuss the corresponding computational complexity using coherent, partially coherent, and incoherent light. We also investigate the expressive power of DONNs and examine how coherence affects their performance. In particular, we show that under fully incoherent illumination, the DONN performance cannot surpass that of a linear model. As a demonstration, we train and evaluate simulated DONNs on the MNIST dataset of handwritten digits using light with varying spatial coherence

Evaluation of NIH consensus criteria for classification of late acute and chronic GVHD

Historically, graft-versus-host disease (GVHD) beyond 100 days after hematopoietic cell transplantation (HCT) was called chronic GVHD, even if the clinical manifestations were indistinguishable from acute GVHD. In 2005, the National Institutes of Health (NIH) sponsored a consensus conference that proposed new criteria for diagnosis and classification of chronic GVHD for clinical trials. According to the consensus criteria, clinical manifestations rather than time after transplantation should be used in clinical trials to distinguish chronic GVHD from late acute GVHD, which includes persistent, recurrent, or late-onset acute GVHD. We evaluated major outcomes according to the presence or absence of NIH criteria for chronic GVHD in a retrospective study of 740 patients diagnosed with historically defined chronic GVHD after allogeneic HCT between 1994 and 2000. The presence or absence of NIH criteria for chronic GVHD showed no statistically significant association with survival, risks of nonrelapse mortality or recurrent malignancy, or duration of systemic treatment. Antecedent late acute GVHD was associated with an increased risk of nonrelapse mortality and prolonged treatment among patients with NIH chronic GVHD. Our results support the consensus recommendation that, with appropriate stratification, clinical trials can include patients with late acute GVHD as well as those with NIH chronic GVHD