This paper is intended to be a resource for programmers needing to optimize a DSP’s power consumption strictly through software. The paper will provide a basic introduction into power consumption background, measurement techniques, and then go into the details of power optimization, focusing on three main areas: algorithmic optimization, taking advantage of hardware features (low power modes, clock control, and voltage control), and data flow optimization with a discussion into the functionality and power considerations when using fast SRAM type memories (common for cache) and DDR SDRAM. This work includes examples and results as tested on Freescale’s current state of the art Digital Signal Processors. / text

Introduction: This paper presented an analysis of the energy consumption and responsiveness of MicroPython in an embedded system. The purpose of this study was to understand the energy consumption and response time of a MicroPython based system to optimize its overall performance and efficiency. Two research questions had been formulated to concretize the purpose of this thesis: [RQ1] How does the energy consumption of a MicroPython based embedded system compare to that of a C-based embedded system for tasks utilizing low power modes? [RQ2] What is the wake-up response time of MicroPython for low power modes when receiving external and internal interrupts, and how does it compare to an established language like C on an embedded system? Method: To answer the research questions and achieve the purpose, an experimental study was conducted. The energy consumption of the MicroPython based system was analyzed under different scenarios. The time it took for MicroPython to respond to an interrupt request from a sleeping state was also measured. The data collected from the experiment was analyzed to determine the level of energy consumption and responsiveness of MicroPython in an embedded system. Results: The results indicated that C was generally more energy efficient and responsive than MicroPython for tasks utilizing low power modes for the Deepsleep mode. Although MicroPython proved to have shorter response times for the Lightsleep low power mode. For energy consumption, C was more stable in the measurements while MicroPython reached both lower minimum currents and higher maximum currents. Conclusions: In conclusion, this study found that while MicroPython could achieve lower power levels than C in both low power modes tested, it reached higher current levels upon waking up. Despite this, MicroPython could still be a choice for applications that spend longer durations in low power modes, as this could offset the increased current spikes during wake-up. Response times for MicroPython were faster than C in the Lightsleep internal interrupt case, but MicroPython exhibited significantly longer response times in the Deepsleep mode due to the system resetting and restarting the interpreter. Keywords: Embedded systems, Energy consumption, Interrupt requests, Low power modes, MicroPython, Responsiveness.

Multi functional health monitoring wearable devices are quite prominent these days. Usually these devices are battery-operated and consequently are limited by their battery life (from few hours to a few weeks depending on the application). Of late, it was realized that these devices, which are currently being operated at fixed voltage and frequency, are capable of operating at multiple voltages and frequencies. By switching these voltages and frequencies to lower values based upon power requirements, these devices can achieve tremendous benefits in the form of energy savings. Dynamic Voltage and Frequency Scaling (DVFS) techniques have proven to be handy in this situation for an efficient trade-off between energy and timely behavior. Within imec, wearable devices make use of the indigenously developed MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). This system is optimized for efficient and accurate collection, processing, and transfer of data from multiple (health) sensors. MUSEIC v2 has limited means in controlling the voltage and frequency dynamically. In this thesis we explore how traditional DVFS techniques can be applied to the MUSEIC v2. Experiments were conducted to find out the optimum power modes to efficiently operate and also to scale up-down the supply voltage and frequency. Considering the overhead caused when switching voltage and frequency, transition analysis was also done. Real-time and non real-time benchmarks were implemented based on these techniques and their performance results were obtained and analyzed. In this process, several state of the art scheduling algorithms and scaling techniques were reviewed in identifying a suitable technique. Using our proposed scaling technique implementation, we have achieved 86.95% power reduction in average, in contrast to the conventional way of the MUSEIC v2 chip’s processor operating at a fixed voltage and frequency. Techniques that include light sleep and deep sleep mode were also studied and implemented, which tested the system’s capability in accommodating Dynamic Power Management (DPM) techniques that can achieve greater benefits. A novel approach for implementing the deep sleep mechanism was also proposed and found that it can obtain up to 71.54% power savings, when compared to a traditional way of executing deep sleep mode. / Nuförtiden så har multifunktionella bärbara hälsoenheter fått en betydande roll. Dessa enheter drivs vanligtvis av batterier och är därför begränsade av batteritiden (från ett par timmar till ett par veckor beroende på tillämpningen). På senaste tiden har det framkommit att dessa enheter som används vid en fast spänning och frekvens kan användas vid flera spänningar och frekvenser. Genom att byta till lägre spänning och frekvens på grund av effektbehov så kan enheterna få enorma fördelar när det kommer till energibesparing. Dynamisk skalning av spänning och frekvens-tekniker (såkallad Dynamic Voltage and Frequency Scaling, DVFS) har visat sig vara användbara i detta sammanhang för en effektiv avvägning mellan energi och beteende. Hos Imec så använder sig bärbara enheter av den internt utvecklade MUSEIC v2 (Multi Sensor Integrated circuit version 2.0). Systemet är optimerat för effektiv och korrekt insamling, bearbetning och överföring av data från flera (hälso) sensorer. MUSEIC v2 har begränsad möjlighet att styra spänningen och frekvensen dynamiskt. I detta examensarbete undersöker vi hur traditionella DVFS-tekniker kan appliceras på MUSEIC v2. Experiment utfördes för att ta reda på de optimala effektlägena och för att effektivt kunna styra och även skala upp matningsspänningen och frekvensen. Eftersom att ”overhead” skapades vid växling av spänning och frekvens gjordes också en övergångsanalys. Realtidsoch icke-realtidskalkyler genomfördes baserat på dessa tekniker och resultaten sammanställdes och analyserades. I denna process granskades flera toppmoderna schemaläggningsalgoritmer och skalningstekniker för att hitta en lämplig teknik. Genom att använda vår föreslagna skalningsteknikimplementering har vi uppnått 86,95% effektreduktion i jämförelse med det konventionella sättet att MUSEIC v2-chipets processor arbetar med en fast spänning och frekvens. Tekniker som inkluderar lätt sömn och djupt sömnläge studerades och implementerades, vilket testade systemets förmåga att tillgodose DPM-tekniker (Dynamic Power Management) som kan uppnå ännu större fördelar. En ny metod för att genomföra den djupa sömnmekanismen föreslogs också och enligt erhållna resultat så kan den ge upp till 71,54% lägre energiförbrukning jämfört med det traditionella sättet att implementera djupt sömnläge.