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Reducing device power consumption (Part 1)

Posted: 12 Aug 2013 ?? ?Print Version ?Bookmark and Share

Keywords:embedded computing? HVAC? energy consumption? network controller?

The pressure for green solutions in embedded computing applications is growing due to rising energy demands from embedded electronics and increasing evidence of global climate change. Despite environmental concerns, expectations for higher performance continue to increase with each new product generation.

Although traditional offline powered equipment such as appliances, lighting systems, and HVAC (heating, ventilation, and air conditioning) dominate electric equipment energy consumption, embedded electronics and online equipment such as printers, storage, networking infrastructure, and data centres are increasingly consuming a larger share of our energy resources. Even equipment that was traditionally offline, such as TVs, refrigerators, and HVAC controls, are now going online, while containing even more embedded processing.

To balance the performance required for powerful new electronic applications with rising concerns over energy consumption, "green" movements and government regulations and programs are driving manufacturers to develop intelligent strategies for optimising performance within specific energy budgets.

Traditional embedded computing platforms have been designed for maximum work load with little regard to the cyclical work profile across hourly, daily, weekly, or extended time intervals. However, new-generation high performance systems are shifting their design focus from provisioning worst-case maximum power loads to optimising for energy efficiency across varying workloads.

Products such as printers are good examples of cyclical workload, as they tend to spend much more time in a ready-to-print state or performing low-workload management services than they do for higher energy consumption printing states. Other embedded applications such as home network gateways, industrial processing plants, and telecommunications systems can employ similar profiling to reduce energy waste and costs too.

As an example, office printers typically print cumulatively only 1 hour out of a 168 hour work week. Without system power management techniques, the 167 hours of idle time power cumulatively can exceed the active state power. Lowering power consumption requires advanced energy management schemes from new product development engineers. A simple strategy to design lower power consuming electronics begins to address the green embedded computing challenge. However, larger gains will come from creating flexible systems that can pace workload with energy consumption in an intelligent and efficient manner.

Previous work has concentrated on analysing and optimising energy for PCs. S. Dedevschi and his coworkers [1] investigated traffic patterns in PC computer networks with the intent of identifying classes of traffic that could potentially be handled by an Autorespond Proxy to reduce idle power. S. Gobriel, C. Maciocco, T.C. Tai [2] introduces some simple heuristics (rather than deep packet inspection) in the context of a NIC connected to a PC to determine when to enable Packet Accumulation. Y. Aggarwal and his co-investigators [3] created a concept called Somniloquy for a type of application-level Autorespond Proxy for PCs. The techniques presented by these authors will be expanded upon later in this series.

However, networked embedded devices have a different set of requirements with more stringent power limits than PCs. Therefore, solutions that require additional hardware components may save significant power in a PC, but the same strategy will not necessarily in an embedded system where the power of the additional hardware components may exceed the power otherwise saved by their use. A potential example of this is the autorespond proxy described later in this paper.

Embedded devices have other characteristics and requirements that differ from PCs. Historically, although the gap is diminishing, processing levels are not as high for embedded devices as for PCs. Other key features of the workload may impact the optimal low power system solution. For example, an embedded processor is less likely to have to perform scheduled virus scans or data backups, and an embedded processor may require a faster wakeup time from a sleep mode than a general-purpose PC. This article concentrates on low power network standby as it relates to embedded systems in the home and office.

Understanding the embedded cyclical states
In most cases, all work performed in embedded computing applications is done in cycles C a combination of active states, management states, and network standby states that are dynamically administered to optimise energy-efficient performance on demand for such applications as high-speed printing, home routers, and WAN managed systems. The various network functions that the system performs in each state are outlined in the figure.

In an embedded networked application, the system spends much of the time in a low-power network standby mode and wakes up in response to an external event. If the system takes too long to wake up, the window for acting on the event that caused the wake up may have closed.

Figure: System states.

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