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May 2008

May 27, 2008

The Efficiency of Moving Bits

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I started my engineering career in the early 1980’s as a designer for a modem company.  It was an amazing time since the Internet was growing in leaps and bounds and personal computers were making their big debut.  In those days the price of modems was similar to the price of gold – roughly $1 US per bit per second.  That’s meant if you wanted a 9600 bps modem (a museum piece today), you’d pay roughly $10,000 US for it.  Those were profitable times. 

The modems of those days were in rack chassis (no custom modem ICs existed yet) with hundreds of 74 series logic devices along with discrete analog filters, modulators, demodulators and line drivers made from op-amps and transistors.  The box weighed about 20 pounds and had a gigantic 50W power supply.  Not only were those early modems expensive to buy, they were very inefficient in moving the bits in terms of the power consumed. 

Things have improved over the last 25 years to where a household network has many high performance personal computers or game stations all connected together with 100 Mbps unshielded twisted pair (UTP) Ethernet drops or even wireless 802.11 access points.  These connections are managed by an Ethernet packet switch and typically a router / firewall connecting to a cable modem to provide upwards of a 10 Mbps connection to the Internet. 

But how would we compare those early (or even more recent) communication technologies to what’s available today.  How should we compare how efficient one technology or component is over another for moving information? Equation 1 provides a simple formula for creating a metric that does just that.    Equation1_3 Power is in watts and the transfer rate (fb) is in bits per second.  The variable “ch” is the number of channels in a system or device to normalize the result to a single channel.  The result is the data transfer efficiency (eb) in joules per bit (J / bit). 

This equation normalizes all coding and signal processing which allows you to compare how good a technology (system or device) is at using the least amount of energy to move a bit across a medium error free (i.e. a bit error rate or BER of less than 10-12).  If you want to normalize the length as well,  Equation2simply divide by the length (in meters) of the connection and the result is Joules per bit-meter (J / bit-m) as shown in Equation 2.  This allows technologies that drive various distances to be compared.

Let’s use equation 2 to calculate the efficiency of that old modem I worked on in the 1980’s.  It was capable of moving 9600 bits per second over 15,000 feet (4572 meters) using roughly 50 watts.  That yields a data transfer efficiency of 1.14 microjoules per bit-meter.  So every bit used roughly 1.14 uJ of energy to move it from my office to the telecom central office 3 miles away (worse case).   If the telephone switch was in the office building next door (1000 feet away), the number goes up to 17.1 uJ / bit-meter.  Compare this with a modern Data Over Cable Interface Specification (DOCSIS) cable modem which uses about 5 watts of power, goes the same distance (using coaxial cable) and moves up to 43 Mbps downstream (using 256 QAM) of error free data.  This equates to a data transfer efficiency of 25.4 picojoules per bit-meter.  That is an multiple improvement in transfer efficiency of more than 40,000 over the old modem technology – an amazing accomplishment.

Next time I’ll cover more on data transfer efficiency and we’ll look at bus architectures and interface technology.  If you have any thoughts (agree / disagree / don’t care), please drop me a comment here on the blog!  Thanks for reading and I hope to hear from you soon!

May 13, 2008

Welcome to the Blog

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Welcome to the EnergyZarr blog… as you can see, my surname is Zarr which very often is confused with Czar or Tsar (Russian) or Zar (German) which translates to “autocratic ruler” – cough… I assure you, the only thing I rule is about 14 square feet of my garage.  However, I did think it was a catchy title, so here we are.

I’m an engineer by education as well as by nature and love to discuss topics related to improving our way of life through technology. This blog is dedicated to discussions on energy efficiency and other related topics such as energy harvesting, lower-power technologies, LED lighting, and so forth.  I will be making weekly updates (maybe more depending on traffic) and look forward to feedback from readers.

This inaugural blog entry examines units of measure for efficiency in analog semiconductor components.  With all the emphasis on making energy-consuming devices more efficient (cars, refrigerators, etc), it makes sense to look at the components that make up these systems.  The issue in the past has been that a specification such as power consumption doesn’t tell the entire story.

For example, imagine two chain saws that both advertise they have a 2-horsepower engine with a 40-minute run time (same size tank).  However, one saw has vanadium-steel chain blades with a ceramic coating that lowers the friction as you cut through a tree.  It also has a revolutionary chain design that more efficiently removes wood as it cuts, making it much faster. The other saw has standard steel blades.  If you simply compared the power consumption of each saw, you might miss the fact that the “high-tech” saw will actually do more work with the same fuel since it will cut faster.  The engine will use less fuel per cut.  So, a better measure of a chainsaw’s efficiency might be cuts per liter of fuel where a “cut” is standardized to mean a 6” diameter branch (or something similar).

In electronics we see the same thing.  A good example is analog-to-digital converters (ADCs).  An engineer designing equipment with many channels of data conversion will often look at the ADC power specifications along with the speed and resolution.  The problem is that not all data converters are equal. There are “marketing bits” and there are “real bits,” also known as the Effective Number of Bits (ENOB).  Like our chain saw analogy, the entire story may not be told with simply comparing the 12-bit, 50 MSPS ADC’s power ratings.

A better way to compare ADCs is to consider how much error the converter contributes to a given power consumption.  This tells you the real story of how good the underlying technology is for efficiently converting AC analog signals to bits.  See equation 1 below – this is a metric that uses the real bits (ENOB) which is a function of both the signal to noise and distortion (SINAD) contributed by the Adc_metric_equation_2 converter.  It also normalizes the number of channels and the frequency at which the converter is running.  The result is energy (in Joules) per conversion.  This is exactly how much energy it costs the system every time a conversion is made.  Now comparing various converters becomes much clearer and even allows dissimilar technologies to be compared (e.g. Flash and folding ADC architectures).

There are many more metrics for other technologies such as serial cable drivers / receivers, power supplies and more.   Check out my white paper on National’s PowerWise family where I talk more in detail about this.  Also, tell me what you think about using metrics such as these to design lower-power systems.   I’d love to hear from you.

Till next time…