Existential threats – ASI part 1

Not sure why but lately I have been hearing a lot about existential events. These are events that threaten the existence of humanity itself.

Massive Solar Storm

A couple of days ago I read about the Carrington Event which was a massive geomagnetic solar storm in 1859. Apparently it wreaked havoc with the communications infrastructure of the time (telegraphs). Researchers have apparently been able to discover other similar events in earth’s history by analyzing ice cores from Greenland which indicate that events of this magnitude occur once every 500 years and smaller events typically occur multiple times/century.

Unclear to me what a solar storm of the magnitude of the Carrington Event would do to the world as we know it today, but we are much more dependent on electronic communications, radio, electronic power, etc. If such an event were to take out, 50% of our electro-magnetic infrastructure, such as frying power transformers, radio transceivers, magnetic storage/motors/turbines, etc. civilization as we know it would be brought back to the mid 1800’s but with a 21st century population.

This would last until we could rebuild all the lost infrastructure, at tremendous cost. During this time we would be dependent on animal-human-water power, paper-optical based communications/storage, and animal-wind transport.

It appears that any optical based communication/computer systems would remain intact but powering them would be problematic without working transformers and generators.

One article (couldn’t locate this) stated that the odds of another Carrington Event happening is 12%  by 2022. But the ice core research seems to indicate that it should be higher than this. By my reckoning, it’s been 155 years since the last event, which means we are ~1/3rd of the way through the next 500 years, so I would expect the probability of a similar event happening to be ~1/3 at this point and rising slightly every year until it happens again.

Superintelligence

I picked up a book called Superintelligence: Paths, Dangers, Strengths by Nick Bostrom last week and started reading it last night. It’s about the dangers of AI gaining the ability to improve itself and after that becoming not just equivalent to Human Level Intelligence (HMLI) but greatly exceeding HMLI at a super-HMLI level (Superintelligent). This means some Superintelligent entity that would have more intelligence than our current population of humans today, by many orders of magnitude.

Bostrom discusses the take off processes that would lead to Superintelligence and some of the ways we could hope to control it. But his belief is that trying to install any of these controls after it has reached HMLI would be fruitless.

I haven’t finished the book but what I have read so far, has certainly scared me.

Bostrom presents three scenarios for a Superintelligence take off: slow take off, fast take off and medium take off. He believes that in a slow take off scenario there may be many opportunities to control the emerging Superintelligence. In a moderate or medium take off, we would know that something is wrong but would have only some limited opportunity to control it. In the fast take off (literally 18months from HMLI to Superintelligence in one scenario Bostrom presents), the likelihood of controlling it after it starts are non-existent.

The later half of Bostrom’s book discusses potential control mechanisms and other ways to moderate the impacts of superintelligence.  So far I don’t see much hope for mankind in the controls he has proposed. But l am only half way through the book and hope to see more substantial mechanisms in the 2nd half.

In the end, any Superintelligence could substantially alter the resources of the world and the impact this would have on humanity is essentially unpredictable. But by looking at recent history, one can see how other species have faired as humanity has altered the resources of the earth. Humanity’s rise has led to massive species die offs, for any species that happened to lie in the way of human progress.

The first part of Bostrom’s book discusses some estimates as to when the world will reach AI with HMLI. Most experts believe that we will see HMLI like this with a 90% probability by the year 2075 and a 50% probability by the year 2050. As for the duration of take off to superintelligence ,the expert opinions are mixed and he believes that they highly underestimate the speed of take off.

Humanity’s risks

The search for extra-terristial intelligence has so far found nothing. One of the parameters for the odds of a successful search was the number of inhabitable planets in the universe. But the another parameter is the ability of a technological civilization to survive long enough to be noticed – the likelihood of a civilization to survive any existential risk that comes up.

Superintelligence and massive solar storms represent just two such risks but there are a multitude of others that can be identified today, and tomorrow’s technological advances will no doubt give rise to more.

Existential risks like these are ever-present and appear to be growing as our technolgical prowess grows. My only problem is that today the study of existential risks seem at best, ad hoc today and at worst, outright disregard.

I believe the best policy is to recognize known existential risks, have some intelligent debate on how probably they are and how we could potentially check them. There really needs to be some systematic study of existential risks around the world bringing academics and technologists together to understand and to mitigate them. The threats to humanity are real, we can continue to ignore them, study a few that gain human interest, or actively seek out and mitigate all of them we can.

Comments?

Photo Credit(s): C3-class Solar Flare Erupts on Sept. 8, 2010 [Detail] by NASA Goddard’s space flight center photo stream

RoS video interview with Ron Redmer Sr. VP Cybergroup

Ray interviewed Ronald Redmer, Sr. VP Cybergroup at EMC’s Global Analyst Summit back in October. Ron is in charge of engineering and product management of their new document analytics service offering. Many of their new service offerings depend on EMC Federation solutions such as ViPR (see my post EMC ViPR virtues & vexations but no virtualization), Pivotal HD, and other offerings.

This was recorded on October 28th in Boston.

New Global Learning XPrize opens

Read a post this week in Gizmag about the new Global Learning XPrize. Past XPrize contests have dealt with suborbital spaceflight, super-efficient automobiles,  oil cleanup, and  lunar landers.

Current open XPrize contests include: Google Lunar Lander, Qualcomm Tricorder medical diagnosis, Nokia Health Sensing/monitoring and Wendy Schmidt Ocean Health Sensing. So what’s left?

World literacy

There are probably a host of issues that the next XPrize could go after but one that might just change the world is to improve children literacy.  According to UNESCO (2nd Global Report on Adult Learning and Education  [GRALE 2]) there are over 250M children of primary school age that will not reach grade 4 levels of education in the world, these children cannot read, write or do basic arithmetic. Given current teaching methods we would need an additional 1.6M teachers to teach all these children. As such, to teach all these children when we include teacher salaries, classroom spaces, supplies, etc. would be highly expensive. There has to be a better, more scaleable way to do this.

Enter the Global Learning XPrize. The intent of this XPrize is to create a tablet application which can teach children how to read, write and do rudimentary arithmetic in 18 months without access to a teacher or other supervised learning.

Where are they in the XPrize process?

The Global Learning XPrize already has raised $15M for the actual XPrize but they are using a crowd funding approach to fund the last $500K which will be used to field test the  Global Learning XPrize candidates. The crowd funding is being done on Indiegogo.

Registration starts now and runs through March 2015, Software development runs through September 2016, at which time five finalists will be selected, each will receive the $1M finalist XPrize to fund a further round of coding. In May of 2017, the five apps will be loaded onto tablets and field testing commences in June 2017 through December 2018. At which time the winner will be selected and will recieve the $10M XPrize.

What other projects have been tried?

I once read an article about the  Hole in the wall computer, where NIIT and their technologists placed an outside hardened, internet connected computer inside a brick wall  in an Indian underprivileged area. The intent was to show that children could learn how to use computers on their own, without adult supervision. Within days children were able to “browse, play games, create documents and paint pictures” on the computer. So minimally invasive education (MIE) can be made to work.

Whats the hardware environment going to look like

There’s no reason that an Android tablet would be any worse and potentially could be much better than a internet connected computer.

Although the tablets will be internet connected it is assumed that the connection will be not always on so the intent is that the apps run standalone as much as possible. Also, I believe that a child will be given a tablet which will be for their exclusive use during the 18 months. The Global Learning XPrize team will insure that there are charging stations where the tablets can be charged once/day but we shouldn’t assume that they can be charged while they are being used.

How are the entries to be judged

The finalists will be judged against EGRA (early grade reading assessment), EGWA (early grade writing assessment), and EGMA (early grad math assessment). The chosen language is to be English and the intent is to use children in countries which have an expressed interest in using English. The Grand winner will be judged to have succeeded if its 7 to 12 year old students can score twice as as well on the EGRA, EGWA and EGMA as a control group. [Not sure what a control group would look like for this nor what they would be doing during the 18 months]. For more information checkout the XPrize guidelines v1 pdf.

The assumption is that there will be about 30 children per village and enough villages will be found to provide a statistically valid test of the five learning apps against a control group.

At the end of all this the winning entry and the other four finalists will have their solutions be open sourced, for the good of the world.

Registration is open now…

Entry applications are $500. Finalists win $1M and the winner will take home $10M.

I am willing to put up the $500 application fee for the Global Learning XPrize. Having never started an open source project, never worked on developing an Android tablet application, or done anything other than some limited professional training this will be entirely new to me – so it should be great fun.  I am thinking of creating a sort of educational video game  (yet another thing I have no knowledge about, :).

We have until March of 2015 to see if we can put a team together to tackle this. I think if I can find four other (great) persons to take this on, we will give it a shot. I hope to enter an application by February of 2015, if we can put together a team by then to tackle this.

Anyone interested in tackling the Global Learning XPrize as an open source project from the gitgo, please comment on this post to let me know.

Photo Credit(s): Kid iPad outside by Alice Keeler

Token ring road traffic control and congestion management

Read an article the other day in Wired, A system to cut traffic that just might work, about two MIT students doing research to help Singapore better manage traffic congestion. They have come up with a sort of token ring network for traffic.

In their approach every car when it enters a “congestion zone” is suppplied an electronic token and when that car leaves the zone it retires it’s token (sound familiar). When the zone is too congested, no new tokens are handed out and cars are re-routed around the zone using GPS provided directions.

It seems a bit hokey but using tokens to control congestion is an old technology and works just fine. The problem with applying tokens to controlling road congestion is that it’s not so easy to re-route someone around a zone if you have to go into it for work or entertainment.

Traffic congestion management today

Most congestion management schemes use congestion toll pricing with transponder and radio transmitters/receivers at entry points into congestion zones. In this fashion metropolitan areas can raise and lower toll pricing on traffic that enters the zone as an incentive to reduce traffic. But this requires special purpose transponders in every car and radio towers at every entry and exit point which fixes the congestion zone boundaries and has a high initial fixed costs.

Singapore’s congestion approach is similar with transponders and radio readers at select entry and exit point locations around the city.

Traffic management via tokens

What the MIT researchers have done is to use a broader WiFi type of radio transmitter in their car transponders with a wider range and use cell tower-like receivers around a metro area to triangulate where a car is and when it’s in a congestion zone and to transfer this information to a central repository.

One advantage to the MIT solution is that the congestion zones are no longer fixed, but can become whatever boundary a city administrator wants to create on a map of the city. This way, different zones could be attempted as experiments whenever it made sense to do so.  Sort of like having a completely configurable congestion zone which can be turned on and off based on the requirements of the moment. And the zones don’t even have to be a polygon at all, any closed form, that could be drawn on a map could represent a new zone.   And of course you could have multiple layers of zones. All this could be almost instantly configurable and trial-able on a whim, like a software defined traffic management (SDTM).

I suppose one problem with using SDTM for toll pricing is that people would need to know ahead of time the cost of traveling through a zone. Maybe that’s why the token approach is better because without a token, you are directed to stop or on another route, outside or around the zone. In one waye tokens could be used as sort of a sophisticated onramp stop signal, that only allows passage when a token frees up.

Maybe token’s should be retired not just when you leave a zone but when you stop moving  or when the engine is turned off as well, that way as cars are parked, their tokens could be freed up for other cars.

How you get people to go along with the token management is another question. As the system is tracking cars automatically, one could automatically fine drivers for violating the token scheme.

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Thank goodness my commute days are long gone.  I get the feeling it’s going to become a lot more interesting driving to work in the future.

Comments?

Photo Credits: World Class Traffic Jam by JosieShowaa

Protest intensity, world news database and big data – chart of the month

Read an article the other day on the analysis of the Arab Spring (Did the Arab Spring really spark a wave of global protests, in Foreign Policy) using a Google Ideas sponsored project, the GDELT ProjectTime domain run chart showing protest intensity every month for the last 30 years, with running average (Global Database of Events, Language and Tone) file of  events extracted from worldwide media sources.  The GDELT database uses sophisticated language processing to extract “event” data from news media streams and supplies this information in database form.  The database can be analyzed  to identify  trends in world events and possibly to better understand what led up to events that occur on our planet.

GDELT Project

The GDELT database records over 300 categories of events that are geo-referenced to city/mountaintop and time-referenced. The event data dates back to 1979.  The GDELT data captures 60 attributes of any event that occurs, generating a giant spreadsheet of event information with location, time, parties, and myriad other attributes all identified, and cross-referenceable.

Besides the extensive spreadsheet of world event attribute data the GDELT project also supplies a knowledge graph oriented view of its event data. The GDELT knowledge graph “compiles a list of every person, organization, company, location and over 230 themes and emotions from every news report” that can then be used to create network diagrams/graphs to be better able to visualize interactions between events. 

For example see the Global Conversation in Foreign Policy, for a network diagram of every person mentioned in the news during 6 months of 2013.  You can zoom in or out to see how people identified in news reports are connected during the six months. So if you we’re interested, in let’s say the Syrian civil war, one could easily see at a glance any news item that mentioned Syria or was located in Syria since 1979 to now. Wow!

Arab Spring and Worldwide Protest

Getting back to the chart-of-the-month, the graphic above shows the “protest intensity” by month for the last 30 years with a running average charted in black using GDELT data.  (It’s better seen in the FP article/link above or just click on it for an expanded view. ).

One can see from the chart that there was a significant increase in protest activity after January 2011, which corresponds to the beginning of the Arab Spring.  But the amazing inference from the chart above is that this increase has continued ever since. This shows that the Arab Spring has had a lasting contribution that has significantly increased worldwide protest activity.

This is just one example of the types of research available with the GDELT data.

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I have talked in the past about how (telecom, social media and other) organizations should deposit their corporate/interaction data sets in some public repository for the better good of humanity so that any researcher could use it (see my Data of the world, lay down your chains post for more on this). The GDELT Project is Google Ideas doing this on a larger scale than I ever thought feasible. Way to go.

Comments?

 Image credits: (c) 2014 ForeignPolicy.com, All Rights Reserved

 

 

Cloud storage growth is hurting NAS & SAN storage vendors

Strange Clouds by michaelroper (cc) (from Flickr)
Strange Clouds by michaelroper (cc) (from Flickr)

My friend Alex Teu (@alexteu), from Oxygen Cloud wrote a post today about how Cloud Storage is Eating the World Alive. Alex reports that all major NAS and SAN storage vendors lost revenue this year over the previous year ranging from a ~3% loss to over a 20% loss (Q1-2014 compared to Q1-2013, from IDC).

Although an interesting development, it’s hard to say that this is the end of enterprise storage as we know it.  I believe there are a number of factors that are impacting  enterprise storage revenues and Cloud storage adoption may be only one of them.

Other trends impacting NAS & SAN storage adoption

One thing that has emerged over the last decade or so is the advance of Flash storage. Some of this is used in storage controllers to speed up IO access and some is used in servers to speed up IO access. But any speedup of IO could potentially reduce the need for high-performing disk drives and could allow customers to use higher capacity/slower disk drives instead. This could definitely reduce the cost of storage systems. A little bit of flash goes  long way to speed up IO access.

The other thing is that disk capacity is trending upward, at exponential rates. Yesterday,s 2TB disk drive is todays 4TB disk drive and we are already seeing 6TB from Seagate, HGST and others. And this is also driving down the cost of NAS and SAN storage.

Nowadays you can configure 1PB of storage with just over 170 drives. Somewhere in there you might want a couple 100TB of Flash to speed up IO access to these slow disks but Flash is also coming down in ($/GB) price (see SanDISK’s recent consumer grade TLC drive at $0.44/GB). Also the move to MLC flash has increased the capacity of flash devices, leading to less SSDs/flash cache cards to store/speed up more data.

Finally, the other trend which seems to have emerged recently is the movement away from enterprise class storage to server storage. One can see this in VMware’s VSAN, HyperConverged systems such as Nutanix and Scale Computing, as well as a general trend in Windows Server applications (SQL Server, Exchange Server, etc.) to make better use of DAS storage. So some customers are moving their data to shared DAS storage today, whereas before this was more difficult to accomplish effectively and because of that they previously purchased networked storage.

What about cloud storage?

Yes, as Alex has noted, the price of cloud storage has declined precipitously over the last year or so. Alex’s cloud storage pricing graph is shows how the entry of Microsoft and Google has seemingly forced Amazon to match their price reductions. But the other thing of note is that they have all come down to about the same basic price of $0.024/GB/Month.

It’s interesting that Amazon delayed their first S3 serious price reductions by about 4 months after Azure and Google Cloud Storage dropped there’s and then within another month after that, they all were at price parity.

What’s cloud storage real growth?

I reported last August that Microsoft Azure and Amazon S3 were respectively storing 8 trillion and over 2 trillion objects (see my Is object storage outpacing structured and unstructured data growth). This year (April 2014) Microsoft mentioned at TechEd that Azure was storing 20 Trillion object and servicing 2 million request per second.

I could find no update to Amazon S3 numbers from last year but the 10x  2.5x growth in Azure’s object count in ~8 months and the roughly doubling of request/second (In my post I didn’t mention last year they were processing 900K requests/second) say something interesting is going on in cloud storage.

I suppose Google’s cloud storage service is too new to report serious results and maybe Amazon wants to keep their growth a secret. But considering Amazon’s recent matching of Azure’s and Google’s pricing, it probably means that their growth wasn’t what they expected.

The other interesting item from the Microsoft discussions on Azure, was that they were already hosting 1M SQL databases in Azure and that 57% of Fortune 500 customers are currently using Azure.

In the “olden days”, before cloud storage, all these SQL databases and Fortune 500 data sets would have more than likely resided on NAS or SAN storage of some kind. And possibly due to the traditional storage’s higher cost and greater complexity, some of this data would never have been spun up in the first place if they had to use traditional storage, but with cloud storage so cheap, rapidly configurable and easy to use all this new data was placed in the cloud.

So I must conclude from Microsofts growth numbers and their implication for the rest of the cloud storage industry that maybe Alex was right, more data is moving to the cloud and this is impacting traditional storage revenues.  With IDC’s (2013) data growth at ~43% per year, it would seem that Microsoft’s cloud storage is growing more rapidly than the worldwide data growth, ~14X faster!

On the other hand, if cloud storage was consuming most of the world’s data growth, it would seem to precipitate the collapse of traditional storage revenues, not just a ~3-20% decline. So maybe the most new cloud storage applications would never have been implemented before if they had to use traditional storage, which means that only some of this new data would ever have been stored on traditional storage in the first place, leading to a relatively smaller decline in revenue.

One question remains: is this a short term impact or more of a long running trend that will play out over the next decade or so? From my perspective, new applications spinning up on non-traditional storage is a long running threat to traditional NAS and SAN storage which will ultimately see traditional storage relegated to a niche. How big this niche will ultimately be and how well it can be defended needs to be the subject for another post?

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Comments?

IBM’s next generation, TrueNorth neuromorphic chip – Neuromorphic Part 2

Ok, I admit it, besides being a storage nut I also have an enduring interest in AI. And as the technology of more sophisticated neuromorphic chips starts to emerge it seems to me to herald a whole new class of AI capabilities coming online. I suppose it’s both a bit frightening as well as exciting which is why it interests me so.

IBM announced a new version of their neuromorphic chip line, called TrueNorth with +5B transistors and the equivalent of ~1M neurons. There were a number of articles on this yesterday but the one I found most interesting was in MIT Technical Review, IBM’s new brainlike chip processes data the way your brain does, (based on a Journal Science article requires login, A million spiking neuron integrated circuit with a scaleable communications network and interface).  We discussed an earlier generation of their SyNAPSE chip in a previous post (see my IBM research introduces SyNAPSE chip post).

But first please take our new poll:

How does TrueNorth compare to the previous chip?

The previous generation SyNAPSE chip had a multi-mode approach which used  65K “learning synapses” together with ~256K “programming synapses”. Their current generation, TrueNorth chip has 256M “configurable synapses” and 1M “programmable spiking neurons”.  So the current chip has quadrupled the previous chips “programmable synapses” and multiplied the “configurable synapses” by a factor of a 1000.

Not sure why the configurable synapses went up so high but it could be an aspect of connectivity, something akin to what happens to a “complete graph” which has a direct edge connection to every node in the graph. In a complete graph if you have N nodes then the number of edges is given as [N*(N-1)]/2, which for 1M nodes would be ~500M edges. So it must not be a complete graph, but it’s “close to complete” with 1/2 the number of edges.

Analog vs. Digital?

When last I talked with IBM on their earlier version chip I wondered why they used digital logic to create it rather than analog. They said to be able to better follow along the technology curve of normal chip electronics digital was the way to go.

It seemed to me at the time that if you really  wanted to simulate a brains neural processing then you would want to use an analog approach and this should use much less power. I wrote a couple of posts on the subject, one of which was on MIT’s analog neuromorphic chip (see my MIT builds analog neuromorphic chip post) and the other was on why analog made more sense than digital technology for neuromorphic computation (see my Analog neural simulation or Digital neuromorphic computing vs. AI post).

The funny thing is that IBM’s TrueNorth chip uses a lot less power (1000X, milliwatts vs watts) than normal CMOS chips in e use today. Not sure why this would be the case with digital logic but if this is true maybe there’s more of a potential to utilize these sorts of chips in wider applications beyond just traditional AI domains.

How do you program it?

I would really like to get a deeper look at the specs for TrueNorth and its programming model.  But there was a conference last year where IBM presented three technical papers on TrueNorth architecture and programming capabilities (see MIT Technical Report: IBM scientists show blueprints for brain like computing).

Apparently the 1M programming spike neurons are organized into blocks of 256 neurons each (with a prodigious amount of “configurable” synapses as well). These seem equivalent to what I would call a computational unit. One programs these blockss with “corelets” which map out the neural activity that the 256-neuron blocks can perform. Also these corelets “programs” can be linked together or one be subsumed within another sort of like subroutines.  IBM as of last year had a library of 150 corelets which do stuff like detect visual artifacts, motion in a visual image, detect color, etc.

Scale-out neuromorphic chips?

The abstract of the Journal Science paper talked specifically about a communications network interface that allows the TrueNorth chips to be “tiled in two dimensions” to some arbitrary size. So it is apparent that with the TrueNorth design, IBM has somehow extended a within chip block interface that allows corelets to call one another, to go off chip as well. With this capability they have created a scale-out model with the TrueNorth chip.

Unclear why they felt it had to go only two dimensional rather than three but, it seems to mimic the sort of cortex layer connections we have in our brains today. But even with only two dimensional scaling there are all sorts of interesting topologies that are possible.

There doesn’t appear to be any theoretical limit to the number of chips that can be connected in this fashion but I would suppose they would all need to be on a single board or at least “close” together because there’s some sort of time frame that couldn’t be exceeded for propagation delay, i.e., the time it takes for a spike to transverse from one chip to the farthest chip in the chain couldn’t exceed say 10msec. or so.

So how close are we to brain level computations?

In one of my previous post I reported Wikipedia stating that  a typical brain has 86B neurons with between 100M and 500M synapses. I was able to find the 86B number reference today but couldn’t find the 100M to 500M synapses quote again.  However, if these numbers are close to the truth, the ratio between human neurons and synapses is much less in a human brain than in the TrueNorth chip. And TrueNorth would need about 86,000 chips connected together to match the neuronal computation of a human brain.

I suppose the excess synapses in the TrueNorth chip is due to the fact that electronic connection have to be fixed in place for a neuron to neuron connection to exist. Whereas in the brain, we can always grow synapse connections as needed. Also, I read somewhere (can’t remember where) that a human brain at birth has a lot more synapse connections than an adult brain and that part of the learning process that goes on during early life is to trim excess synapses down to something that is more manageable or at least needed.

So to conclude, we (or at least IBM) seem to be making good strides in coming up with a neuromorphic computational model and physical hardware, but we are still six or seven generations away from a human brain’s capabilities (assuming a 1000 of these chips could be connected together into one “brain”).  If a neuromorphic chip generation takes ~2 years then we should be getting pretty close to human levels of computation by 2028 or so.

The Tech Review article said that the 5B transistors on TrueNorth are more transistors than any other chip that IBM has produced. So they seem to be at current technology capabilities with this chip design (which is probably proof that their selection of digital logic was a wise decision).

Let’s just hope it doesn’t take it 18 years of programming/education to attain college level understanding…

Comments?

Photo Credit(s): New 20x [view of mouse cortex] by Robert Cudmore

Extremely low power transistors open up new IoT applications

We have written before about the computational power efficiency law know as Koomley’s Law which states that the computations one can do with the same amount of energy has been doubling every 1.57 years (for more info, please see my No power sensors surface … post).

The dawn of sub-threshold electronics

But just this week there was another article this time about electronics that use much less power than normal transistors. Achieving this in Internet of Thing (IoT) type sensors would take the computations/joule up by a orders of magnitude, not just ~1.6X as in Koomley’s law, although how long it will take to come out commercially is another issue

This new technology is called sub-threshold transistors and they use much less power than normal transistors. The article in MIT Technical Review, A batteryless sensor chip for the IoT, discusses the phenomenon used by sub-threshold transistors that normal transistors, even when they are technically in the “off” state, leak some amount of current.  This CMOS transistor parasitic leakage had been considered a current drain that couldn’t be eliminated and as such, wasted energy up until recently.

Not so any longer, with the new sub-threshold transistor design paradigm, electronics  could now take advantage of this “leakage” current to perform actual computations. And that opens up a whole new level of IoT sensors that could be deployed.

Prototype sub-threshold circuits coming out

One company PsiKick is using this phenomenon to design ASIC/chips that, depending on the application, using sub-threshold transistors plus extensive power reduction design techniques, only use 0.1 to 1% of the energy of similar functioning chips. Their first prototype was a portable EKG that uses body heat to power itself with a thermo-electric generator rather than a battery.  The prototype was just a proof of concept but they seem to be at work trying to open the technology to broader applications.

One serious consideration limiting the types of sensors that could be deployed in IoT applications was how to get power to these sensors. The other thing was how to get information out of the sensor and out to the real world.  There are a few ways to attack the power issue for IoT sensors, creating more efficient electronics, more effective/long lasting batteries, and smaller electronic generators. Sub-threshold transistor electronics takes a major leap forward to more efficient electronics.

In my previous post we discussed ways to construct smaller electronic generators used by low-power systems/chips. One approach highlighted in that paper used small antennas to extract power from ambient radio waves. But that’s not the only way to generate small amounts of power. I have also heard of piezoelectric generators that use force and movement (such as foot falls) to generate energy. And of course, small solar panels could do the same trick.

Any of these micro energy generators could be made to work, and together with the ability to design circuits that use 0.1 to 1% of the electricity used by normal circuits, this  should just about eliminate any computational/power limits to the sorts of IoT sensors that could be deployed.

What about non-sensor/non-IoT electronics?

Not sure if this works for IoT sensors why it couldn’t be used for something more substantial like mobile/smart phones, desktop computers, enterprise servers, etc. To that end, it seems that ARM Holdings and IMEC are also looking at the technology.

Only a couple of years ago, everybody was up in arms about all the energy consumption of server farms, especially on the west coast of the USA. But with this sort of sub-threshold transistor electronics coming online, maybe servers could run on ambient radio wave energy, data centers could run desktop computers and led lighting off of thermo-electric generators inside their heat exchangers, and iPhones could run off of accelerometer piezoelectric generators using the motion a phone undergoes while sitting in a pocket of a moving person.

Almost gives the impression of perpetual motion machines but rather than motion we are talking electronics, sort of like perpetual electronics…

So can a no-battery iPhone be in our future, I wouldn’t bet against it. Remember, the compute engine inside all iPhones is based on ARM technology.

Comments?

Photo credit(s): Intel Free Press: Joshua R. Smith holding a sensor