Embedded DNN Classifier for Five Different Cardiac Diseases

The evolution of modern healthcare has been signif- icantly shaped by the convergence of connected sensors, smart Wearable Devices, Artificial Intelligence, and the Internet of Things giving rise to the domain of eHealth and offering invalu- able insights into the complications of heart health. eHealth’s im- pact extends to facilitating diagnosis, treatment, and medication for a diverse array of conditions, prominently including cardiac diseases. Despite substantial strides in medical technology, the detection of arrhythmia remains a persistent challenge, with early diagnosis holding the potential to avert numerous fatalities. This paper proposes an ultra-lightweight (876KB) Embedded- Deep Neural Network model specifically designed for resource- constrained devices. With high accuracy ranging from 94% to 99% for five classes identified from the MIT-BIH dataset, the proposed model is small enough to fit on tiny devices like the Arduino Nano BLE 33 Sense. This translates to low power consumption and real-time inference, making it ideal for screening cardiac diseases on wearable devices

An Energy-Efficient HSI Analysis Accelerator on FPGAs for Satellite Imagery

Hyper-Spectral imaging (HSI) is a crucial technique used to analyse remote sensing data acquired from Earth observa- tion satellites. The rich spatial and spectral information obtained through HSI allows for better characterisation and exploration of the Earth’s surface over traditional techniques like RGB and Multi-Spectral imaging on the downlinked image data at ground stations. In some cases, these images do not contain meaningful information due to the presence of clouds or other artefacts, limiting their usefulness. Transmission of such artefact HSI im- ages leads to wasteful use of already scarce energy and time costs required for communication. While detecting such artefacts prior to transmitting the HSI image is desirable, the computational complexity of these algorithms and the limited power budget on satellites (especially CubeSats) are key constraints. This paper presents an unsupervised learning-based convolutional autoencoder (CAE) model for artefact identification of acquired HSI images at the satellite and a deployment architecture on AMD’s Zynq Ultrascale FPGAs. The model is trained and tested on widely used HSI image datasets: Indian Pines, Salinas Valley, the University of Pavia and the Kennedy Space Center. For deployment, the model is quantised to 8-bit precision, fine-tuned using the Vitis-AI framework and integrated as a subordinate accelerator using AMD’s Deep-Learning Processing Units (DPU) instance on the Zynq device. Our tests show that the model can process each spectral band in an HSI image in 4ms, 2.6× better than INT8 inference on Nvidia’s Jetson platform &amp; 16.1× better than SOTA artefact detectors. Our model also achieves an f1-score of 92.8% and FPR of 0% across the dataset, while consuming 21.52 mJ per HSI image, 3.6× better than INT8 Jetson inference &amp; 7.5× better than SOTA artefact detectors, making it a viable architecture for deployment in CubeSats.<br/

Architecture exploration of FPGA based accelerators for bioinformatics applications

This book presents an evaluation methodology to design future FPGA fabrics incorporating hard embedded blocks (HEBs) to accelerate applications. This methodology will be useful for selection of blocks to be embedded into the fabric and for evaluating the performance gain that can be achieved by such an embedding. The authors illustrate the use of their methodology by studying the impact of HEBs on two important bioinformatics applications: protein docking and genome assembly. The book also explains how the respective HEBs are designed and how hardware implementation of the application is done using these HEBs. It shows that significant speedups can be achieved over pure software implementations by using such FPGA-based accelerators. The methodology presented in this book may also be used for designing HEBs for accelerating software implementations in other domains besides bioinformatics. This book will prove useful to students, researchers, and practicing engineers alike

Hardware acceleration of de Novo genome assembly

International audienceThe cost of genome assembly has gone down drastically with the advent of next generation sequencing technologies. These new sequencing technologies produce large amounts of DNA fragments. Software programs are used to construct the genome from these DNA fragments. The assembly programs take significant amount of time to execute. To reduce the execution time, these programs are being parallelised to take advantage of many cores available in present day processor chips. Further, hardware accelerators have been developed which when used along with processors speed up the execution. Velvet is a commonly used software for de novo assembly. We propose a novel method to reduce the overall time of assembly by using FPGAs. In this method, we perform pre-processing of these short reads on FPGAs and process the output using Velvet to reduce the overall time for assembly. We show that using our technique we can get significant speed-ups

First narrow-band search for continuous gravitational waves from known pulsars in advanced detector data

International audienceSpinning neutron stars asymmetric with respect to their rotation axis are potential sources of continuous gravitational waves for ground-based interferometric detectors. In the case of known pulsars a fully coherent search, based on matched filtering, which uses the position and rotational parameters obtained from electromagnetic observations, can be carried out. Matched filtering maximizes the signal-to-noise (SNR) ratio, but a large sensitivity loss is expected in case of even a very small mismatch between the assumed and the true signal parameters. For this reason, narrow-band analysis methods have been developed, allowing a fully coherent search for gravitational waves from known pulsars over a fraction of a hertz and several spin-down values. In this paper we describe a narrow-band search of 11 pulsars using data from Advanced LIGO’s first observing run. Although we have found several initial outliers, further studies show no significant evidence for the presence of a gravitational wave signal. Finally, we have placed upper limits on the signal strain amplitude lower than the spin-down limit for 5 of the 11 targets over the bands searched; in the case of J1813-1749 the spin-down limit has been beaten for the first time. For an additional 3 targets, the median upper limit across the search bands is below the spin-down limit. This is the most sensitive narrow-band search for continuous gravitational waves carried out so far

Search for intermediate mass black hole binaries in the first observing run of Advanced LIGO

International audienceDuring their first observational run, the two Advanced LIGO detectors attained an unprecedented sensitivity, resulting in the first direct detections of gravitational-wave signals produced by stellar-mass binary black hole systems. This paper reports on an all-sky search for gravitational waves (GWs) from merging intermediate mass black hole binaries (IMBHBs). The combined results from two independent search techniques were used in this study: the first employs a matched-filter algorithm that uses a bank of filters covering the GW signal parameter space, while the second is a generic search for GW transients (bursts). No GWs from IMBHBs were detected; therefore, we constrain the rate of several classes of IMBHB mergers. The most stringent limit is obtained for black holes of individual mass 100  M⊙, with spins aligned with the binary orbital angular momentum. For such systems, the merger rate is constrained to be less than 0.93  Gpc−3 yr−1 in comoving units at the 90% confidence level, an improvement of nearly 2 orders of magnitude over previous upper limits