Multitask real-time embedded systems are often restricted by tight energy budgets, whilst they usually have environmental interactions through software-controlled energy-hungry peripheral modules like LTE, WiFi, and GSM. The way that the driver calls are used within the embedded software to do such a control introduces program energy-hotspots (EHs) from the peripheral module perspective, namely the code pieces wasting the system energy. By the energy waste, we mean that the energy consumption is reducible via some program code modifications without threatening the system schedulability and logical correctness. This paper examines the program EHs of fixed-priority real-time tasks where two types of energy inefficiency can occur: Intra-task type, causing energy waste even if a task runs individually, and inter-task type, happening due to the interaction between different system tasks, namely preemption scenarios even if there is no intra-task EH. The main cause of such EHs is the unnecessary time intervals between the driver calls, causing extra energy consumption by peripheral modules. We propose some static analysis methods to automatically detect and eliminate both types of intra-and inter-task EHs regarding their mutual relevance, according to the extreme (worst-case and best-case) execution times of certain task code parts. Our manipulations on the tasks to eliminate the EHs include some program code modifications with the awareness of system schedulability and logical correctness, and changing some scheduling decisions, namely limiting the preemption points. After applying our proposed method to the test tasks, our simulation results show an energy reduction of up to 19 percent.

Most modern multi-core edge devices work in outdoor situations with limited power supplies like energy harvester and batteries. Therefore, energy consumption is a fundamental issue in which the memory subsystem has a significant role. Scratchpad memories (SPM) can provide a broad potential for energy saving. Still, due to the insufficient SPM capacity in such edge devices, a rigorous SPM data allocation scheme is necessary to reduce the energy consumption of the memory subsystem. Emerging non-volatile memories (NVMs) are very useful to reduce the energy consumption of the memory subsystem. Compared with SRAM, NVMs have lower leakage power and higher density, but the read and write latencies of the NVMs are higher than the SRAM. Therefore, embedded and edge devices can take advantage of hybrid SPM composed of both NVM and SRAM to achieve further energy saving. This paper proposes MASTER, a task mapping, task scheduling, and dynamic SPM allocation scheme that efficiently utilizes the hybrid SPM space. To this end, we model the hybrid SPM allocation on a multi-core system with integer linear programming formulation to minimize the energy consumption of the memory subsystem. Experimental results show that MASTER improves the energy saving of the memory subsystem by up to 34 percent compared to EADA, which is a heuristic dynamic data allocation algorithm for multi-core systems with hybrid SPM.

Today’s deeply embedded systems, with real-time interactions to the environment, are largely battery-operated, and peripheral modules like LTE, WiFi, and GPS are among the most energy-hungry components of them. These components are often under the direct control of an embedded software. Some pieces of the software program are called energy hotspots if they can be transformed towards better system energy consumption while leaving it logically- and temporally-correct. This paper focuses on three such energy hotspots from the peripheral module perspective. The root causes of the hotspots in the software program are misplaced driver calls: Early acquiring or late releasing of the module causes it to waste energy in the active state, having unnecessary distance between the use operations causes extra tail energy overhead, and unaccounted releasing and re-acquiring of the module causes more energy consumption in comparison to leaving the module unreleased. We provide static analysis methods for the detection and elimination of such energy hotspots automatically with regard to some relations between temporal requirements of the real-time embedded software, the time and energy specifications of the module, and the extreme (worst-case/best-case) execution times of specific pieces of the software program. Our simulation results show about 4.7 to 20 percent of energy reductions after elimination of the energy hotspots of the test programs using our proposed method.