Vitamin-V: Virtual Environment and Tool-boxing for Trustworthy Development of RISC-V based Cloud Services

Vitamin-V is a 2023-2025 Horizon Europe project that aims to develop a complete RISC-V open-source software stack for cloud services with comparable performance to the cloud-dominant x86 counterpart and a powerful virtual execution environment for software development, validation, verification, and test that considers the relevant RISC-V ISA extensions for cloud deployment

Automatic Distributed Deep Learning Using Resource-constrained Edge Devices

Processing data generated at high volume and speed from the Internet of Things, smart cities, domotic, intelligent surveillance, and e-healthcare systems require efficient data processing and analytics services at the Edge to reduce the latency and response time of the applications. The Fog Computing Edge infrastructure consists of devices with limited computing, memory, and bandwidth resources, which challenge the construction of predictive analytics solutions that require resource-intensive tasks for training machine learning models. In this work, we focus on the development of predictive analytics for urban traffic. Our solution is based on deep learning techniques localized in the Edge, where computing devices have very limited computational resources. We present an innovative method for efficiently training of Gated Recurrent-Units (GRUs) across available resource-constrained CPU and GPU Edge devices. Our solution employs distributed GRU model learning and dynamically stops the training process to utilize the low-power and resource-constrained Edge devices while ensuring good estimation accuracy effectively. The proposed solution was extensively evaluated using low-powered ARM-based devices, including Raspberry Pi v3 and the low-powered GPU-enabled device NVIDIA Jetson Nano, and also compared them with Single-CPU Intel Xeon machines. For the evaluation experiments, we used real-world Floating Car Data. The experiments show that the proposed solution delivers excellent prediction accuracy and computational performance on the Edge when compared with the baseline methods

Means and standard deviation of normalized electromyographic amplitude for four recording sites of the superficial masseter muscle during bites at different percentages of maximal voluntary bite force by healthy volunteers (n = 20).

<p>%: percent of maximal voluntary bite force.</p><p>^AStatistically significant difference between the Anterior and Posterior columns (p-value <0.05).</p><p>^BStatistically significant difference between the Anterior and Middle-posterior columns (p-value = 0.001).</p><p>^CStatistically significant difference between the Middle-anterior and Posterior columns (p-value = 0.01).</p><p>Means and standard deviation of normalized electromyographic amplitude for four recording sites of the superficial masseter muscle during bites at different percentages of maximal voluntary bite force by healthy volunteers (n = 20).</p

Study design.

<p>RDCTMD: Research diagnostic criteria for temporomandibular disorders. MVBF: Maximal voluntary bite force. SMVBF: Submaximal voluntary bite force. HDEMGs: High density surface electromyography.</p

Show the matrix of electrodes used and the topographic maps of the amplitude of the EMG activity of the superficial masseter.

<p>(A) Matrix of 24 surface electrodes arranged in four columns: anterior (A), middle-anterior (MA), middle-posterior (MP) and posterior (P) columns. C: cantus, G: gonion. (B) Examples of topographic maps of the amplitude of the EMG activity of the superficial masseter recorded during bites at 20, 40, 60 and 80% of voluntary maximum bite force (VMBF). Maps were constructed in windows of 500 ms and with an interpolation factor of 8. Amplitude values of each map are expressed as a percentage of the maximum value of each one. ○: electrode positions. *: location of center of mass. *: location of center of mass at 20% of VMBF.</p