BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning

Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation algorithm (BP). This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pre-trained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high performance deep learning training. We present the design for a convolutional neural network, BPLight-CNN, which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. The proposed design achieves (i) at least 34x speedup, 34x improvement in computational efficiency, and 38.5x energy savings, during training; and (ii) 29x speedup, 31x improvement in computational efficiency, and 38.7x improvement in energy savings, during inference compared to the state-of-the-art designs. All these comparisons are done at a 16-bit resolution; and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared to the state-of-the-art

Synthesis of a stable gold hydrosol by the reduction of chloroaurate ions by the amino acid, aspartic acid

Development of reliable protocols for the synthesis of nanoparticles of well-defined sizes and good monodispersity is an important aspect of nanotechnology. In this paper, we present details of the synthesis of gold nanoparticles of good monodispersity by the reduction of aqueous chloroaurate ions by the amino acid, aspartic acid. The colloidal gold solution thus formed is extremely stable in time, indicating electrostatic stabilization via nanoparticle surface-bound amino acid molecules. This observation has been used to modulate the size of the gold nanoparticles in solution by varying the molar ratio of chloroaurate ions to aspartic acid in the reaction medium. Characterization of the aspartic acid-reduced gold nanoparticles was carried out by UV-visible spectroscopy, thermogravimetric analysis and transmission electron microscopy. The use of amino acids in the synthesis and stabilization of gold nanoparticle in water has important implications in the development of new protocols for generation of bioconjugate materials

One-step synthesis of hydrophobized gold nanoparticles of controllable size by the reduction of aqueous chloroaurate ions by hexadecylaniline at the liquid-liquid interface

Vigorous stirring of a biphasic mixture containing hexadecylaniline in chloroform and aqueous chloroauric acid results in the formation of gold nanoparticles of controllable size in the organic phase

Growth of TiO<SUB>2</SUB> nanoparticles in thermally evaporated fatty amine thin films by a method of ion entrapment

The synthesis of titania nanoparticles within thermally evaporated octadecylamine (ODA) thin films is described. Synthesis of the nanoparticles was achieved by electrostatically entrapping TiF62&#8722; ions in thin films of the fatty amine by a simple immersion technique followed by in-situ hydrolysis of the metal ions. Without any further heat treatment, it was observed that titania nanoparticles of the brookite polymorph were formed within the film. By this simple procedure, uniformly distributed fairly monodisperse titania nanoparticles of ca. 4 nm diameter were synthesized within the lipid matrix and investigated using a host of techniques

Infrared based saliva screening test for COVID-19

Abstract: Severe acute respiratory syndrome coronavirus 2 has resulted in an unprecedented need for diagnostic testing that is critical in controlling the spread of COVID-19. We propose a portable infrared spectrometer with purpose-built transflection accessory for rapid point-of-care detection of COVID-19 markers in saliva. Initially, purified virion particles were characterized with Raman spectroscopy, synchrotron infrared (IR) and AFM-IR. A data set comprising 171 transflection infrared spectra from 29 patients testing positive for SARS-CoV-2 by RT-qPCR and 28 testing negative, was modeled using Monte Carlo Double Cross Validation with 50 randomized test and model sets. The testing sensitivity was 93 % (27/29) with a specificity of 82 % (23/28) that included positive samples on the limit of detection for RT-qPCR. Here, we demonstrate a proof-of-concept high throughput infrared COVID-19 test that is rapid, inexpensive, portable and utilizes sample self-collection thus minimizing the risk to healthcare workers and ideally suited to mass screening

Severe akathisia as a side effect of metoclopramide

Case description A case of severe metoclopramide-induced akathisia in a breast cancer patient being treated with chemotherapy is presented, eventually culminating in hospital admission. In retrospect, this adverse effect was not recognized for several weeks because the prescription had not been properly recorded in the chart, the patient initially denied using the drug, and extensive psychological adjustment difficulties were also present. Conclusion Movement disorders as an adverse effect of metoclopramide have been described on a regular basis over the past decades. Case reports such as this confirm there is under-recognition of adverse effects and emphasize the need to take a comprehensive medication history and recognize well known side effects of medications such as metoclopramide