Abstract
Modern hardware accelerators, such as FPGAs, allow offloading large regions of C/C++ code in order to improve the execution time and/or the energy consumption of software applications. An outstanding challenge with this approach, however, is solving the Hardware/Software (Hw/Sw) partitioning problem. Given the increasing complexity of both the accelerators and the potential code regions, one needs to adopt a holistic approach when selecting an offloading region by exploring the interplay between communication costs, data usage patterns, and target-specific optimizations. To this end, we propose representing a C application as an extended task graph (ETG) with flexible granularity, which can be manipulated through the merging and splitting of tasks. This approach involves generating a task graph overlay on the program's Abstract Syntax Tree (AST) that maps tasks to functions and the flexible granularity operations onto inlining/outlining operations. This maintains the integrity and readability of the original source code, which is paramount for targeting different accelerators and enabling code optimizations, while allowing the offloading of code regions of arbitrary complexity based on the data patterns of their tasks. To evaluate the ETG representation and its compiler, we use the latter to generate ETGs for the programs in Rosetta and MachSuite benchmark suites, and extract several metrics regarding data communication, task-level parallelism, and dataflow patterns between pairs of tasks. These metrics provide important information that can be used by Hw/Sw partitioning methods.

Abstract

Abstract
Plants gather and process information about their surroundings to make decisions that prioritize their well-being while considering the environment. These decisions are conveyed through electrical signals within and between cells, mainly in the form of action and variation potentials, in response to stimuli, including mechanical vibrations, changes in temperature, light intensity, and humidity. Although the ability of some plants, such as the Mimosa pudica, to react to sudden environmental stimuli (e.g., touch) is well known, their long-term electrical response under slow environmental changes remains not fully understood. Here, a multi-source monitoring system has been developed to collect and store electrical signals from the plant Mimosa pudica, and surrounding environmental temperature and humidity, over a period of approximately 5 days. A realtime dashboard shows the environmental temperature and variation potential (VP) from Mimosa pudica. The VP mimics the environmental temperature changes, with an associated delay. Our long-term physiological observations suggest that environmental temperature sensing in the plant Mimosa pudica can be monitored and is likely driven by bioelectricity.

Abstract
Human Activity Recognition (HAR) has been a popular research field due to the widespread of devices with sensors and computational power (e.g., smartphones and smartwatches). Applications for HAR systems have been extensively researched in recent literature, mainly due to the benefits of improving quality of life in areas like health and fitness monitoring. However, since persons have different motion patterns when performing physical activities, a HAR system would need to adapt to the characteristics of the user in order to maintain or improve accuracy. Mobile devices, such as smartphones, used to implement HAR systems, have limited resources (e.g., battery life). They also have difficulty adapting to the device's constraints to work efficiently for long periods. In this work, we present a kNN-based HAR system and an extensive study of the influence of hyperparameters (window size, overlap, distance function, and the value of k) and parameters (sampling frequency) on the system accuracy, energy consumption, and response time. We also study how hyperparameter configurations affect the model's performance for the users and the activities. Experimental results show that adapting the hyperparameters makes it possible to adjust the system's behavior to the user, the device, and the target service. These results motivate the development of a HAR system capable of automatically adapting the hyperparameters for the user, the device, and the service.

Abstract
This article presents Kadabra, a Java source-to-source compiler that allows users to make code queries, code analysis and code transformations, all user-programmable using the domain-specific language LARA. We show how Kadabra can be used as the basis for developing a runtime autotuning and adaptivity framework, able to adapt existing source Java code in order to take advantage of runtime autotuning. Specifically, this article presents the framework, consisting of Kadabra and an API for runtime adaptivity. We show the use of the framework to extend Java applications with autotuning and runtime adaptivity mechanisms to target performance improvement and/or energy saving goals.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).