Energy efficient implementation of machine learning algorithms on hardware platforms

Machine and deep learning algorithms are currently employed for many applications such as computer vision, speech recognition and portable/wearable electronics. Regrettably, machine learning algorithms have high complexity adding more challenges for the implementation of such algorithms on embedded hardware platforms. This paper aims to present an overview about state of the art techniques enabling efficient implementation of Machine and Deep learning (ML/DL) algorithms aiming to improve the energy efficiency. An assessment of the algorithms suitable for embedded implementation is provided, presenting some hardware platforms supporting artificial intelligent systems. On the other hand, we have exploited the choice of implementing ML/DL algorithm on embedded hardware platforms

Smart tactile sensing systems based on embedded CNN implementations

Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent. This paper presents and compares the implementations of a convolutional neural network model for tactile data decoding on various hardware platforms. Experimental results show comparable classification accuracy of 90.88% for Model 3, overcoming similar state-of-the-art solutions in terms of time inference. The proposed implementation achieves a time inference of 1.2 ms while consuming around 900 \u3bcJ. Such an embedded implementation of intelligent tactile data decoding algorithms enables tactile sensing systems in different application domains such as robotics and prosthetic devices

Touch Modality Classification Using Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are mainly designed to deal with sequence prediction problems and they show their effectiveness in processing data originally represented as time series. This paper investigates the time series characteristics of RNNs to classify touch modalities represented as spatio temporal 3D tensor data. Different approaches are followed in order to propose efficient RNN models aimed at tactile data classification. The main idea is to capture long-term dependence from data that can be used to deal with long sequences represented by employing Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures. Moreover, a case specific approach to dataset organization of the 3D tensor data is presented. The target is to provide efficient hardware-friendly touch modality classification approaches suitable for embedded applications. To this end, the proposed work achieves effective performance in terms of hardware complexity by reducing the FLOPS by 99.98% and the memory storage by 98.34%, with respect to the state-of-art solutions on the same benchmark dataset. This directly affects the time latency and energy consumption of the embedded hardware. Besides, the implemented models shows a classification accuracy higher than those state-of-art solutions. Results demonstrate that the proposed computing architecture is scalable showing acceptable complexity when the system is scaled up in terms of input matrix size and number of classes to be recognized

Approximate Computing Methods for Embedded Machine Learning

Embedding Machine Learning enables integrating intelligence in recent application domains such as Internet of Things, portable healthcare systems, and wearable devices. This paper presents an assessment of approximate computing methods at algorithmic, architecture, and circuit levels and draws perspectives for further developments and applications. The main goal is to investigate how approximate computing may reduce the complexity and enable the feasibility of embedded Machine Learning (ML) systems. Though ML is a powerful paradigm for applications in the perceptual domain (i.e. vision, touch, hearing, etc.), their computational complexity is very high and consequently real time operation and ultra-low power are still very challenging objectives. On the other hand, approximate computing has emerged as an effective solution to reduce hardware complexity, time latency and to increase energy efficiency. \ua9 2018 IEEE

PTEN R130Q Papillary Tumor of the Pineal Region (PTPR) with Chromosome 10 Loss Successfully Treated with Everolimus: A Case Report

Papillary tumors of the pineal region (PTPR) can be observed among adults with poor prognosis and high recurrence rates. Standards of therapy involve total surgical excision along with radiation therapy, with no promising prospects for primary adjuvant chemotherapy, as long-term treatment options have not been explored. Chromosome 10 loss is characteristic of PTPR, and PTEN gene alterations are frequently encountered in a wide range of human cancers and may be treated with mTORC1 inhibitors such as everolimus. In parallel, there are no reports of treating PTPR with everolimus alone as a monopharmacotherapy. We report the case of a patient diagnosed with PTPR (grade III) characterized by a PTEN R130Q alteration with chromosome 10 loss that was treated with everolimus pharmacotherapy alone, resulting in an asymptomatic course and tumor regression, a rare yet notable phenomenon not described in the literature so far with potential to alter the management approach to patients with PTPR

Live Demonstration: System based on Electronic Skin and Cutaneous Electrostimulation for Sensory Feedback in Prosthetics

To restore the sense of touch in upper limb prosthetics, a prosthetic device can be equipped with tactile sensors providing data to be transmitted to the user using either invasive or non-invasive interfaces. This demo will be based on our sensing - noninvasive stimulation feedback system [1]. It will show two important aspects of our technology: 1) High sensitivity: light touch detection will be enabled by the high sensitivity of electronic skin (e-skin) prototypes for fingertips, 2) Measuring complex interactions: different contact shapes and multiple contact points will be detected by the commercial e-skin prototype suitable for palm

Impact of Commercialized Genomic Tests on Adjuvant Treatment Decisions in Early Stage Breast Cancer Patients

Introduction. Advances in genomic techniques have been valuable in guiding decisions regarding the treatment of early breast cancer (EBC) patients. These multigene assays include Oncotype DX, Prosigna, and Endopredict. There has generally been a tendency to overtreat or undertreat patients, and having reliable prognostic factors could significantly improve rates of appropriate treatment administration. In this study, we showcase the impact of genomic tests on adjuvant treatment decisions in EBC patients. Materials and Methods. This is a retrospective study that includes EBC patients treated between December 2016 and February 2018. The physician’s choice of treatment was recorded before and after obtaining the results of the genomics tests. Baseline demographics and pathological data were collected from medical records. Results. A total of 75 patients were included. Fifty patients underwent Oncotype DX genomic analysis, 11 patients underwent Prosigna analysis, and 14 patients underwent Endopredict analysis. A total of 21 physicians’ plans (28%) were initially undecided and then carried out after obtaining genomic test results. 13 patients were planned to undergo endocrine therapy alone, while 8 were planned to undergo both endocrine therapy and chemotherapy. Treatment was changed in 26 patients (34.67%). The decision to deescalate therapy was taken in 19 patients (25.33%). The decision to escalate treatment was made in 7 patients (9.33%). Conclusion. Our study demonstrates the importance of genomics testing, as it assisted physicians in avoiding unnecessary adjuvant chemotherapy in 25.33% of patients, thus reducing side effects of chemotherapy and the financial burden on patients

Management of patients with high-risk and advanced prostate cancer in the Middle East:resource-stratified consensus recommendations

PURPOSE Prostate cancer care in the Middle East is highly variable and access to specialist multidisciplinary management is limited. Academic tertiary referral centers offer cutting-edge diagnosis and treatment; however, in many parts of the region, patients are managed by non-specialists with limited resources. Due to many factors including lack of awareness and lack of prostate-specific antigen (PSA) screening, a high percentage of men present with locally advanced and metastatic prostate cancer at diagnosis. The aim of these recommendations is to assist clinicians in managing patients with different levels of access to diagnostic and treatment modalities. METHODS The first Advanced Prostate Cancer Consensus Conference (APCCC) satellite meeting for the Middle East was held in Beirut, Lebanon, November 2017. During this meeting a consortium of urologists, medical oncologists, radiation oncologist and imaging specialists practicing in Lebanon, Syria, Iraq, Kuwait and Saudi Arabia voted on a selection of consensus questions. An additional workshop to formulate resource-stratified consensus recommendations was held in March 2019. RESULTS Variations in practice based on available resources have been proposed to form resource-stratified recommendations for imaging at diagnosis, initial management of localized prostate cancer requiring therapy, treatment of castration-sensitive/naïve advanced prostate cancer and treatment of castration-resistant prostate cancer. CONCLUSION This is the first regional consensus on prostate cancer management from the Middle East. The following recommendations will be useful to urologists and oncologists practicing in all areas with limited access to specialist multi-disciplinary teams, diagnostic modalities and treatment resources