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Low-latency inference for machine learning models is increasingly becoming a necessary requirement, as these models are used in mission-critical applications such as autonomous driving, military defense (e.g., target recognition), and network traffic analysis. A widely studied and used technique to overcome this challenge is to offload some or all parts of the inference tasks onto specialized hardware such as graphic processing units. More recently, offloading machine learning inference onto programmable network devices, such as programmable network interface cards or a programmable switch, is gaining interest from both industry and academia, especially due to the latency reduction and computational benefits of performing inference directly on the data plane where the network packets are processed. Yet, current approaches are relatively limited in scope, and there is a need to develop more general approaches for mapping offloading machine learning models onto programmable network devices. To fulfill such a need, this work introduces a novel framework, called ML-NIC, for deploying trained machine learning models onto programmable network devices' data planes. ML-NIC deploys models directly into the computational cores of the devices to efficiently leverage the inherent parallelism capabilities of network devices, thus providing huge latency and throughput gains. Our experiments show that ML-NIC reduced inference latency by at least 6 × on average and in the 99th percentile and increased throughput by at least 16xwith little to no degradation in model effectiveness compared to the existing CPU solutions. In addition, ML-NIC can provide tighter guaranteed latency bounds in the presence of other network traffic with shorter tail latencies. Furthermore, ML-NIC reduces CPU and host server RAM utilization by 6.65% and 320.80 MB. Finally, ML-NIC can handle machine learning models that are 2.25 × larger than the current state-of-the-art network device offloading approaches. 

The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this article, we focus on the effectiveness of machine learning models to translate the colorimetric reaction to kidney health prediction. In this setting, we thoroughly evaluated the effectiveness of our novel proposed models against state-of-the-art classical machine learning and deep learning approaches. Additionally, we executed a number of ablation studies to measure the performance of our model when trained using different meta-parameter choices. Our evaluation results indicate that our selective partitioned regression (SPR) model, using histogram of colors-based features and a histogram gradient boosted trees underlying estimator, exhibits much better overall prediction performance compared to state-of-the-art methods. Our initial study indicates that SPR can be an effective tool for detecting the severity of kidney disease using inexpensive lateral flow assay test strips and a smart phone-based application. Additional work is needed to verify the performance of the model in various settings. 

Abstract With the aim of analyzing large-sized multidimensional single-cell datasets, we are describing a method for Cosine-based Tanimoto similarity-refined graph for community detection using Leiden’s algorithm (CosTaL). As a graph-based clustering method, CosTaL transforms the cells with high-dimensional features into a weighted k-nearest-neighbor (kNN) graph. The cells are represented by the vertices of the graph, while an edge between two vertices in the graph represents the close relatedness between the two cells. Specifically, CosTaL builds an exact kNN graph using cosine similarity and uses the Tanimoto coefficient as the refining strategy to re-weight the edges in order to improve the effectiveness of clustering. We demonstrate that CosTaL generally achieves equivalent or higher effectiveness scores on seven benchmark cytometry datasets and six single-cell RNA-sequencing datasets using six different evaluation metrics, compared with other state-of-the-art graph-based clustering methods, including PhenoGraph, Scanpy and PARC. As indicated by the combined evaluation metrics, Costal has high efficiency with small datasets and acceptable scalability for large datasets, which is beneficial for large-scale analysis.