A fundamental challenge for running machine learning algorithms on battery-powered devices is the time and energy limitations, as these devices have constraints on resources. There are resource-efficient classifier algorithms that can run on these devices, but their accuracy is often sacrificed for resource efficiency. Here, we propose an ultra-low power classifier, SEFR, with linear time complexity, both in the training and the testing phases. SEFR is comparable to state-of-the-art classifiers in terms of classification accuracy, but it is 63 times faster and 70 times more energy efficient than the average of state-of-the-art and baseline classifiers on binary class datasets. The energy and memory consumption of SEFR is very insignificant, and it can even perform both train and test phases on microcontrollers. To our knowledge, this is the first multipurpose classification algorithm specifically designed to perform both training and testing on ultra-low power devices.
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research, Agency for Science, Technology and Research（科技研究局资讯通信研究院）；Indian Institute of Science（印度科学学院）
This paper presents a novel method for information interpretability in an MC-SEFRON classifier. To develop a method to extract knowledge stored in a trained classifier, first, the binary-class SEFRON classifier developed earlier is extended to handle multi-class problems. MC-SEFRON uses the population encoding scheme to encode the real-valued input data into spike patterns. MC-SEFRON is trained using the same supervised learning rule used in the SEFRON. After training, the proposed method extracts the knowledge for a given class stored in the classifier by mapping the weighted postsynaptic pot…
Time series shapelets are discriminative subsequences and their similarity to a time series can be used for time series classification. Since the discovery of time series shapelets is costly in terms of time, the applicability on long or multivariate time series is difficult. In this work we propose Ultra-Fast Shapelets that uses a number of random shapelets. It is shown that Ultra-Fast Shapelets yield the same prediction quality as current state-of-the-art shapelet-based time series classifiers that carefully select the shapelets by being by up to three orders of magnitudes. Since this method…
By mimicking brain-like cognition and exploiting parallelism, hyperdimensional computing (HDC) classifiers have been emerging as a lightweight framework to achieve efficient on-device inference. Nonetheless, they have two fundamental drawbacks, heuristic training process and ultra-high dimension, which result in sub-optimal inference accuracy and large model sizes beyond the capability of tiny devices with stringent resource constraints. In this paper, we address these fundamental drawbacks and propose a low-dimensional computing (LDC) alternative. Specifically, by mapping our LDC classifier i…
The pattern of Electroencephalogram (EEG) signal differs significantly across different subjects, and poses challenge for EEG classifiers in terms of 1) effectively adapting a learned classifier onto a new subject, 2) retaining knowledge of known subjects after the adaptation. We propose an efficient transfer learning method, named Meta UPdate Strategy (MUPS-EEG), for continuous EEG classification across different subjects. The model learns effective representations with meta update which accelerates adaptation on new subject and mitigate forgetting of knowledge on previous subjects at the sam…
Fast model updates for unseen tasks on intelligent edge devices are crucial but also challenging due to the limited computational power. In this paper,we propose MetaLDC, which meta-trains braininspired ultra-efficient low-dimensional computing classifiers to enable fast adaptation on tiny devices with minimal computational costs. Concretely, during the meta-training stage, MetaLDC meta trains a representation offline by explicitly taking into account that the final (binary) class layer will be fine-tuned for fast adaptation for unseen tasks on tiny devices; during the meta-testing stage, Meta…
Columbia University（哥伦比亚大学）；Samsung Electronics（三星电子）
Always-on artificial intelligent (AI) functions such as keyword spotting (KWS) and visual wake-up tend to dominate total power consumption in ultra-low power devices. A key observation is that the signals to an always-on function are sparse in time, which a spiking neural network (SNN) classifier can leverage for power savings, because the switching activity and power consumption of SNNs tend to scale with spike rate. Toward this goal, we present a novel SNN classifier architecture for always-on functions, demonstrating sub-300nW power consumption at the competitive inference accuracy for a KW…
The preservation of our cultural heritage is of paramount importance. Thanks to recent developments in digital acquisition techniques, powerful image analysis algorithms are developed which can be useful non-invasive tools to assist in the restoration and preservation of art. In this paper we propose a semi-supervised crack detection method that can be used for high-dimensional acquisitions of paintings coming from different modalities. Our dataset consists of a recently acquired collection of images of the Ghent Altarpiece (1432), one of Northern Europe's most important art masterpieces. Our …
Sri Sivasubramaniya Nadar College of Engineering（斯里西瓦苏布拉马尼亚纳达尔工程学院）
Neonatal seizures are a commonly encountered neurological condition. They are the first clinical signs of a serious neurological disorder. Thus, rapid recognition and treatment are necessary to prevent serious fatalities. The use of electroencephalography (EEG) in the field of neurology allows precise diagnosis of several medical conditions. However, interpreting EEG signals needs the attention of highly specialized staff since the infant brain is developmentally immature during the neonatal period. Detecting seizures on time could potentially prevent the negative effects on the neurocognitive…
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Although Printed Electronics (PE) cannot compete with silicon-based systems in conventional evaluation metrics, e.g., integration density, area and performance, PE offers attractive properties such as on-demand ultra-low-cost fabrication, flexibility and non-toxicity. As a result, it targets application domains that are untouchable by lithography-based silicon electronics and thus have not yet seen much proliferation of computing. However, despite the attractive characteristics of PE, the large feature sizes in PE prohibit the realization of complex printed circuits, such as Machine Learning (…
Indian Institute of Science（印度科学学院）；Washington University in St. Louis（圣路易斯华盛顿大学）
The new generation of machine learning processors have evolved from multi-core and parallel architectures that were designed to efficiently implement matrix-vector-multiplications (MVMs). This is because at the fundamental level, neural network and machine learning operations extensively use MVM operations and hardware compilers exploit the inherent parallelism in MVM operations to achieve hardware acceleration on GPUs and FPGAs. However, many IoT and edge computing platforms require embedded ML devices close to the network in order to compensate for communication cost and latency. Hence a nat…
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA
Istituto Nazionale di Fisica Nucleare (INFN)（国家核物理研究所）；Dipartimendo di Fisica e Astronomia (DFA)（物理与天文学系）；University of Padua（帕多瓦大学）；Dipartimendo di Ingegneria dell’Informazione (DEI)（信息工程系）；Dipartimendo di Ingegneria Industriale (DII)（工业工程系）；Padua Quantum Technology Research Center（帕多瓦量子技术研究中心）；Tensor AI Solutions GmbH（张量人工智能解决方案有限公司）
RockNet: Distributed Learning on Ultra-Low-Power Devices
Institute for Data Science in Mechanical Engineering, RWTH Aachen University（机械工程数据科学研究所，亚琛RWTH大学）；Networked Embedded Systems Lab, TU Darmstadt（网络嵌入式系统实验室，德累斯顿技术大学）
Nowadays government and private agencies use remote sensing imagery for a wide range of applications from military applications to farm development. The images may be a panchromatic, multispectral, hyperspectral or even ultraspectral of terra bytes. Remote sensing image classification is one amongst the most significant application worlds for remote sensing. A few number of image classification algorithms have proved good precision in classifying remote sensing data. But, of late, due to the increasing spatiotemporal dimensions of the remote sensing data, traditional classification algorithms …
SSN College of Engineering（SSN工程学院）；Solarillion Foundation（Solarillion基金会）
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide, with 2% of the population affected. It is associated with an increased risk of strokes, heart failure and other heart-related complications. Monitoring at-risk individuals and detecting asymptomatic AF could result in considerable public health benefits, as individuals with asymptomatic AF could take preventive measures with lifestyle changes. With increasing affordability to wearables, personalized health care is becoming more accessible. These personalized healthcare solutions require accurate classification of bio-…
GMem: A Modular Approach for Ultra-Efficient Generative Models
Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；School of Engineering, Westlake University（西湖大学工程学院）；Zhejiang University（浙江大学）
Application of artificial intelligence in medicine brings in highly accurate predictions achieved by complex models, the reasoning of which is hard to interpret. Their generalization ability can be reduced because of the lack of pixel wise annotated images that occurs in frozen section tissue analysis. To partially overcome this gap, this paper explores the approximate explicit feature map (aEFM) transform of low-dimensional data into a low-dimensional subspace in Hilbert space. There, with a modest increase in computational complexity, linear algorithms yield improved performance and keep int…
Institute for Nuclear Research of the Russian Academy of Sciences（俄罗斯科学院核研究所）；Moscow Institute for Physics and Technology（莫斯科物理技术学院）；Sternberg Astronomical Institute, Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学施滕贝格天文研究所）；Lebedev Physical Institute（列别杰夫物理研究所）；Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学斯科别尔岑核物理研究所）
We present a method to analyse arrival directions of ultra-high-energy cosmic rays (UHECRs) using a classifier defined by a deep convolutional neural network trained on a HEALPix grid. To illustrate a high effectiveness of the method, we employ it to estimate prospects of detecting a large-scale anisotropy of UHECRs induced by a nearby source with an (orbital) detector having a uniform exposure of the celestial sphere and compare the results with our earlier calculations based on the angular power spectrum. A minimal model for extragalactic cosmic rays and neutrinos by Kachelrieß, Kalashev, Os…
