So you are excited about the release of a new ARM-powered smartphone or tablet device – and why shouldn’t you be! You’ve made your way to your preferred tech review website where you discover that the device is running big.LITTLE™ technology – sweet! Though hang-on, it’s also running the latest version of ARM big.LITTLE software “big.LITTLE MP”. So what additional benefits does big.LITTLE MP bring compared to its predecessor? In this blog, I attempt to answer this frequently asked question.
The mobile analytics firm, Flurry, carried out an analysis on smartphone users in the US and made some interesting findings. The study found that mobile users spend most of their time on the following mobile activities:
Calculated on a daily basis, web browsing and ‘facebooking’ accounted for 38% of a mobile user’s smartphone interaction time, gaming accounted for 32% and the use of audio, video and utility services was third in line at 16%. In total, the top three activities account for a staggering 86% of the time we spend on smartphones, and goes to show how far the mobile use case has come from the times when mobile phones were used plainly for voice calls and text messaging.
But how do these use cases impact on power consumption? By looking at the power profile (i.e. the power vs. time) for each activity, a pattern begins to emerge that highlights three very distinct patterns.
For the web browser analysis, we used the BBench browser benchmark provided by the University of Michigan. BBench simulates browsing popular websites of varying richness and complexity, and enables key parameters to be configured. In order to ensure reliable results were obtained, we ran the workload with a clear environment for maximum accuracy and reproducibility. To maximize the reproducibility, execution of these workloads and related measurement were automated. The following graph shows the power profile that we produced from a run on a Symmetrical Multi Processing (SMP) system consisting of a Quad-core Cortex-A7 CPU subsystem.
Graph 1: Power profile of web browsing use case
The first thing you will notice about the power profile (Graph 1) is the spikes in power. These typically occur when launching an application, loading content or scrolling through webpages. In other words, they occur when the system requires a short burst of performance to respond to a user interaction. Responsiveness is a type of user experience metric and therefore the better your mobile system is at handling such workloads, the better the overall mobile user experience.
For the mobile gaming workload, we ran the popular gaming application CastleMaster. Through workload analysis, we selected a period of gameplay that produced high intensity performance load which was automated to ensure reproducibility. The following graph shows the power profile produced from this workload from a run on an SMP system consisting of a Quad-core Cortex-A7 CPU subsystem.
Graph 2: Power profile of web browsing use case
The power profile here requires a more constant level of power, which is common in intensive gaming applications, where the CPU cores are required to process a high amount of multi-threaded data for the GPU cores. In workloads like these, as you can imagine, power efficiency within the thermal budget of the system is vital.
To demonstrate MP3 audio playback, we played a freely available MP3 audio sample on the default Android music player. The following graph shows the power profile that we produced from this workload from a run on an SMP system consisting of a Quad-core Cortex-A7 CPU subsystem.
Graph 3: Power profile of web browsing use case
Workloads such as audio playback and video playback are known as low intensity workloads and tend to have long use periods. Power savings is therefore essential to having a longer battery life.
Analysing the patterns in the power profile from each of the mobile applications above, we are able to identify three main building blocks, each present with a high degree of prominence across the workloads:
Graph 4: Power profiles of the building blocks in the top three mobile use cases
Graph 4 shows a conglomeration of each of these categories. We are able to observe a high degree of power and performance requirements in today’s mobile applications, particularly in the three classes of mobile activities that we spend most of our time on. In real life, a mobile user is usually listening to an MP3 audio playback while surfing the web or watching an embedded video while using Facebook. In such instances, we would expect a combination of these three classes of workloads. In order to be able to handle the requirements of such a mix of workloads efficiently, a combination of high performance and high power efficiency cores working seamlessly in a single mobile system is required.
This is where big.LITTLE Technology comes in. big.LITTLE Technology is a power optimization technology that, through the combination of high performance "big" cores and high efficiency "LITTLE" cores, along with big.LITTLE MP software, ensures the right task is run on the right core. This delivers increased levels of power efficiency, battery life and user experience. Graph 5 shows a comparison of the degree of improvement on average that big.LITTLE MP delivers when compared to its predecessor, Cluster Migration.
Graph 5: big.LITTLE MP improvement over big.LITTLE Cluster Migration
If you are keen to find out more about how big.LITTLE MP is able to achieve these improvements, I will be delving into this topic in my "big.LITTLE Unleashed" presentation at this year's ARM TechCon event, held next week (October 1st-3rd). If you have not registered for it yet, be sure to register for TechCon now.
If you are unable to make it, however, then fear not! In my next blog, I will dive deeper into the details of how big.LITTLE MP is able to achieve these improvements and show how it enables you to enjoy a higher quality mobile experience.
Nice blog post, very interesting stuff! Can you provide a link to the mobile study that Flurry carried out? Thanks