Introduction to Programming Using Mobile Phones and MIT App Inventor

At the beginning of each year, we ask our new undergraduate students in Computer Engineering if they have ever developed a computer program. Surprisingly, the most frequent answer is no. The few students who have attended a Computer Science training module usually have some basic programming notions; however, most of our students coming straight from high school have never programmed. This lack of basic programming skills represents a major drawback when taking programming-related courses. This is especially true for the course on Computer Organization, taught during the first semester of the first year, as one of its main objectives is to explain the processor architecture, and therefore a great part of it revolves around programming in assembly language. To tackle this lack of basic programming skills, a workshop on mobile application programming using MIT App Inventor is offered to freshmen. This workshop is highly welcomed and positively received by the students, and we believe that it has contributed to improving their performance on courses related to programming, and in particular, on the Computer Organization course

Multi-Armed Bandits for Spectrum Allocation in Multi-Agent Channel Bonding WLANs

While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied to the primary and secondary channel selection. Unfortunately, in uncoordinated high-density deployments where multiple basic service sets (BSSs) may potentially overlap, hand-crafted spectrum management techniques perform poorly given the complex hidden/exposed nodes interactions. To cope with such challenging Wi-Fi environments, in this paper, we first identify machine learning (ML) approaches applicable to the problem at hand and justify why model-free RL suits it the most. We then design a complete RL framework and call into question whether the use of complex RL algorithms helps the quest for rapid learning in realistic scenarios. Through extensive simulations, we derive that stateless RL in the form of lightweight multi-armed-bandits (MABs) is an efficient solution for rapid adaptation avoiding the definition of broad and/or meaningless states. In contrast to most current trends, we envision lightweight MABs as an appropriate alternative to the cumbersome and slowly convergent methods such as Q-learning, and especially, deep reinforcement learning

Mobile Edge Vertical Applications Using ETSI MEC APIs and Sandbox

MEC Sandbox is an excellent tool that simulates wireless networks and deploys ETSI Multi-access Edge Computing (MEC) APIs on top of the simulated wireless network. In this demo, we consume these APIs using a decision engine (DE) to scale a video-on-demand (VoD) application located on the network edge, assuming that the average number of users is a good proxy of the demand. Specifically, the developed DE uses the ETSI MEC Location API and retrieves the number of users in a given zone. The DE then takes actions at the microservice scaling level and executes them through a custom-made Kubernetes-based OpenAPI.Comment: A one-page version of this paper is accepted in the 2022 IEEE Conference on Standards for Communications and Networking(CSCN)-Demo submission

Towards Energy Efficient LPWANs through Learning-based Multi-hop Routing

Low-power wide area networks (LPWANs) have been identified as one of the top emerging wireless technologies due to their autonomy and wide range of applications. Yet, the limited energy resources of battery-powered sensor nodes is a top constraint, especially in single-hop topologies, where nodes located far from the base station must conduct uplink (UL) communications in high power levels. On this point, multi-hop routings in the UL are starting to gain attention due to their capability of reducing energy consumption by enabling transmissions to closer hops. Nonetheless, a priori identifying energy efficient multi-hop routings is not trivial due to the unpredictable factors affecting the communication links in large LPWAN areas. In this paper, we propose epsilon multi-hop (EMH), a simple reinforcement learning (RL) algorithm based on epsilon-greedy to enable reliable and low consumption LPWAN multi-hop topologies. Results from a real testbed show that multi-hop topologies based on EMH achieve significant energy savings with respect to the default single-hop approach, which are accentuated as the network operation progresses

Intent-Based Orchestration for Application Relocation in a 5G Cloud-native Platform

The need of mobile network operators for cost-effectiveness is driving 5G and beyond networks towards highly flexible and agile deployments to adapt to dynamic and resource-constrained scenarios while meeting a myriad of user network stakeholders' requirements. In this setting, we consider that zero-touch orchestration schemes based on cloud-native deployments equipped with end-to-end monitoring capabilities provide the necessary technology mix to be a solution candidate. This demonstration, built on top of an end-to-end cloud-native 5G experimental platform with over-the-air transmissions, shows how dynamic orchestration can relocate container-based end-user applications to fulfil intent-based requirements. Accordingly, we provide an experimental validation to showcase how the platform enables the desired flexible and agile 5G deployments

Animaciones interactivas para la enseñanza y aprendizaje de los protocolos de coherencia de cachés

Entre los objetivos formativos de los cursos avanzados de arquitectura de computadores suele estar el de que los estudiantes sean capaces de describir y analizar el funcionamiento de los protocolos de coherencia de cachés. Aunque dichos protocolos son relativamente sencillos, es necesario analizar muchas situaciones diferentes para entender cómo abordan todos los detalles del problema que quieren resolver. Lo que hace que sean complejos de explicar y de comprender. Una herramienta que ilustrara gráficamente el funcionamiento de dichos protocolos facilitaría enormemente su enseñanza/aprendizaje. Con objeto de mejorar la docencia de dicha materia, hemos desarrollado tres animaciones interactivas que muestran cómo funcionan tres de los protocolos de coherencia de caché más frecuentemente utilizados. Para cada protocolo, una serie de operaciones de lectura/escritura ilustran todas las posibles situaciones que pueden darse. Las animaciones permiten avanzar y retroceder para poder entender/estudiar mejor las acciones que tienen lugar en cada paso.SUMMARY: Among the educational objectives in advanced courses of computers architecture there is usually one that states that students should be able to describe and analyze how the cache coherence protocols work. Although these protocols are relatively simple, it is necessary to analyze many different situations to understand how they address all the details of the problem they solve. This makes them complex to be explained and to be understood. A tool that illustrates graphically the operation of these protocols should greatly facilitate the teaching/learning of these protocols. With the aim of improving the teaching on this subject, we have developed three interactive animations that show how some of the most frequently used cache coherence protocols work. For each protocol, a sequence of read and write operations illustrates all possible situations that can take place in each protocol. The tool is interactive in that the student can go forward and backward to understand/ study the different actions that occur at each step.Peer Reviewe

Optimising Convolutions for Deep Learning Inference on ARM Cortex-M Processors

We perform a series of optimisations on the convo lution operator within the ARM CMSIS-NN library to improve the performance of deep learning tasks on Arduino development boards equipped with ARM Cortex-M4 and M7 microcontrollers. To this end, we develop custom microkernels that efficiently handle the internal computations required by the convolution operator via the lowering approach and the direct method, and we design two techniques to avoid register spilling. We also take advantage of all the RAM on the Arduino boards by reusing it as a scratchpad for the convolution filters. The integration of these techniques into CMSIS-NN, when invoked by TensorFlow Lite for microcontrollers for quantised versions of VGG, SqueezeNet, ResNet, and MobileNet-like convolutional neural networks enhances the overall inference speed by a factor ranging from 1.13× to 1.50×.This research was funded by project TED2021-129334B-I00 supported by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”. Manuel F. Dolz was also supported by the Plan Gen–T grant CIDEXG/2022/13 of the Generalitat Valenciana. Antonio Macia-Lillo is a PRE2021-099284 fellow supported by MCIN/AEI/10.13039/501100011033