Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference

Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a 8×8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy)

Pump-probe measurement of atomic parity violation in caesium with a precision of 2.6%

We present the atomic parity violation measurements made in Cs vapour using a pump-probe scheme. After pulsed excitation of the 6S-7S forbidden transition in the presence of a longitudinal electric field, a laser beam resonant with one of the 7S-6P transitions stimulates the 7S atom emission for a duration of 20 ns. The polarisation of the amplified probe beam is analysed. A seven-fold signature allows discrimination of the parity violating linear dichroism, and real-time calibration by a similar, known, parity conserving linear dichroism. The zero-field linear dichroism signal due to the magnetic dipole transition moment is observed for the first time, and used for in-situ determination of the electric field. The result, ImE1^{pv}= (-808+/- 21) 10^{-14} ea\_{0}, is in perfect agreement with the corresponding, more precise measurement obtained by the Boulder group. A transverse field configuration with large probe amplification could bring atomic parity violation measurements to the 0.1% accuracy level.Comment: "conference PAVI 06, Milos, Greece, May 2006

Predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks

In modern data centers, storage system failures are major contributors to downtimes and maintenance costs. Predicting these failures by collecting measurements from disks and analyzing them with machine learning techniques can effectively reduce their impact, enabling timely maintenance. While there is a vast literature on this subject, most approaches attempt to predict hard disk failures using either classic machine learning solutions, such as Random Forests (RFs) or deep Recurrent Neural Networks (RNNs). In this work, we address hard disk failure prediction using Temporal Convolutional Networks (TCNs), a novel type of deep neural network for time series analysis. Using a real-world dataset, we show that TCNs outperform both RFs and RNNs. Specifically, we can improve the Fault Detection Rate (FDR) of ≈ 7.5% (FDR = 89.1%) compared to the state-of-the-art, while simultaneously reducing the False Alarm Rate (FAR = 0.052%). Moreover, we explore the network architecture design space showing that TCNs are consistently superior to RNNs for a given model size and complexity and that even relatively small TCNs can reach satisfactory performance. All the codes to reproduce the results presented in this paper are available at https://github.com/ABurrello/tcn-hard-disk-failure-prediction

Transfer of recessive skr crossability trait into well-adapted French wheat cultivar Barok through marker-assisted backcrossing method

In order to increase genetic diversity in cereals, interspecific or even intergeneric crosses are worthwhile, especially wheat by rye crosses for triticale production. However, these crosses often fail due to inhibiting genes. To overcome this obstacle, crossability trait, present in a few wheat cultivars, can be transferred into other wheat lines of agronomical interest. Nevertheless, this transfer remains tedious through conventional backcrossing methods because it is a recessive trait, which requires selfing generations and complex evaluation by many crosses. Here, we present a marker-assisted backcrossing method to transfer this trait more quickly and easily. We chose to introduce the recessive crossability skr, located on chromosome 5BS and originating from Asian wheat, into Barok, a non-crossable French wheat cultivar, with good agronomic characteristics. Six molecular markers, close to the Skr locus, were used to check the transfer of the gene at each of the three backcrosses, without selfing generation nor crosses with rye. Finally, we crossed the predicted crossable lines with rye to validate their crossability. We obtained sixteen lines, morphologically similar to Barok, exhibiting high crossability rate (30%). The markers were thus efficient to transfer the skr crossability but they remain too far from the Skr locus to be considered as diagnostic markers. Indeed, genotyping and phenotyping on other wheat cultivars showed some discrepancies. Nevertheless, this opens the way to enhance genetic diversity more easily and to improve traits of agronomic interest in triticale or wheat as well as to study further barriers to intergeneric crosses

Energy-efficient adaptive machine learning on IoT end-nodes with class-dependent confidence

Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency, and improve privacy. An effective way to obtain energy-efficiency with small accuracy drops is to sequentially execute a set of increasingly complex models, early-stopping the procedure for 'easy' inputs that can be confidently classified by the smallest models. As a stopping criterion, current methods employ a single threshold on the output probabilities produced by each model. In this work, we show that such a criterion is sub-optimal for datasets that include classes of different complexity, and we demonstrate a more general approach based on per-classes thresholds. With experiments on a low-power end-node, we show that our method can significantly reduce the energy consumption compared to the single-threshold approach

Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes

Quantization is widely employed in both cloud and edge systems to reduce the memory occupation, latency, and energy consumption of deep neural networks. In particular, mixed-precision quantization, i.e., the use of different bit-widths for different portions of the network, has been shown to provide excellent efficiency gains with limited accuracy drops, especially with optimized bit-width assignments determined by automated Neural Architecture Search (NAS) tools. State-of-The-Art mixed-precision works layer-wise, i.e., it uses different bit-widths for the weights and activations tensors of each network layer. In this work, we widen the search space, proposing a novel NAS that selects the bit-width of each weight tensor channel independently. This gives the tool the additional flexibility of assigning a higher precision only to the weights associated with the most informative features. Testing on the MLPerf Tiny benchmark suite, we obtain a rich collection of Pareto-optimal models in the accuracy vs model size and accuracy vs energy spaces. When deployed on the MPIC RISC-V edge processor, our networks reduce the memory and energy for inference by up to 63% and 27% respectively compared to a layer-wise approach, for the same accuracy

A new Manifestation of Atomic Parity Violation in Cesium: a Chiral Optical Gain induced by linearly polarized 6S-7S Excitation

We have detected, by using stimulated emission, an Atomic Parity Violation (APV) in the form of a chiral optical gain of a cesium vapor on the 7S - 6P$_{3/2}$ transition,consecutive to linearly polarized 6S-7S excitation. We demonstrate the validity of this detection method of APV, by presenting a 9% accurate measurement of expected sign and magnitude. We underline several advantages of this entirely new approach in which the cylindrical symmetry of the set-up can be fully exploited. Future measurements at the percent level will provide an important cross-check of an existing more precise result obtained by a different method.Comment: 4 pages, 2 figure

Improving PPG-based Heart-Rate Monitoring with Synthetically Generated Data

Improving the quality of heart-rate monitoring is the basis for a full-time assessment of people’s daily care. Recent state-of-the-art heart-rate monitoring algorithms exploit PPG and inertial data to efficiently estimate subjects’ beats-per-minute (BPM) directly on wearable devices. Despite the easy-recording of these signals (e.g., through commercial smartwatches), which makes this approach appealing, new challenges are arising. The first problem is fitting these algorithms into low-power memory-constrained MCUs. Further, the PPG signal usually has a low signal-to-noise ratio due to the presence of motion artifacts (MAs) arising from movements of subjects’ arms. In this work, we propose using synthetically generated data to improve the accuracy of PPG-based heart-rate tracking using deep neural networks without increasing the algorithm’s complexity. Using the TEMPONet network as baseline, we show that the HR tracking Mean Absolute Error (MAE) can be reduced from 5.28 to 4.86 BPM on PPGDalia dataset. Noteworthy, to do so, we only increase the training time, keeping the inference step unchanged. Consequently, the new and more accurate network can still fit the small memory of the GAP8 MCU, occupying 429 KB when quantized to 8bits

Ultra-compact binary neural networks for human activity recognition on RISC-V processors

Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning models such as decision trees and Random Forests (RFs), whereas deep learning is less common due to its high computational complexity. In this work, we propose a novel implementation of HAR based on deep neural networks, and precisely on Binary Neural Networks (BNNs), targeting low-power general purpose processors with a RISC-V instruction set. BNNs yield very small memory footprints and low inference complexity, thanks to the replacement of arithmetic operations with bit-wise ones. However, existing BNN implementations on general purpose processors impose constraints tailored to complex computer vision tasks, which result in over-parametrized models for simpler problems like HAR. Therefore, we also introduce a new BNN inference library, which targets ultra-compact models explicitly. With experiments on a single-core RISC-V processor, we show that BNNs trained on two HAR datasets obtain higher classification accuracy compared to a state-of-the-art baseline based on RFs. Furthermore, our BNN reaches the same accuracy of a RF with either less memory (up to 91%) or more energy-efficiency (up to 70%), depending on the complexity of the features extracted by the RF

Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition

Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms. Gesture recognition exploiting surface electromyographic (sEMG) signals is one of the most promising approaches, given that sEMG signal acquisition is non-invasive and is directly related to muscle contraction. However, the analysis of these signals still presents many challenges since similar gestures result in similar muscle contractions. Thus the resulting signal shapes are almost identical, leading to low classification accuracy. To tackle this challenge, complex neural networks are employed, which require large memory footprints, consume relatively high energy and limit the maximum battery life of devices used for classification. This work addresses this problem with the introduction of the Bioformers. This new family of ultra-small attention-based architectures approaches state-of-the-art performance while reducing the number of parameters and operations of 4.9 ×. Additionally, by introducing a new inter-subjects pre-training, we improve the accuracy of our best Bioformer by 3.39 %, matching state-of-the-art accuracy without any additional inference cost. Deploying our best performing Bioformer on a Parallel, Ultra-Low Power (PULP) microcontroller unit (MCU), the GreenWaves GAP8, we achieve an inference latency and energy of 2.72 ms and 0.14 mJ, respectively, 8.0× lower than the previous state-of-the-art neural network, while occupying just 94.2 kB of memory