VMworld2017’s forecast, cloudy with a high chance of containers

Attended VMworld2017 this past week in Vegas and aside from all the parties there was a lot of news, mostly for public cloud users.

In talking with analysts and others at the show it seems like VMware has recently discovered that they can’t fight the cloud, so they better join them. Early this year VMware divested itself of its vCloud Air Business to OVH, which removed their owned competition to the cloud. Now, VMware’s on a different tack, figuring out how to best work with today’s public cloud providers and implementing this.

Last year VMware announced an agreement with IBM and to supply vCloud Air services on IBM’s SoftLayer public cloud. This year, VMware ramps up other public cloud offerings with VMware Cloud on AWS and PKS (Pivotal Container Services) on vSphere.

First up, VMware on the (AWS) cloud

You may recall that earlier this year VMware showed a tech preview of vSphere running in AWS. At VMworld2017 they took off the wraps on this service and made it real. At first it’s only available in AWS US WEST region but they plan to roll it out to the rest of US soon and rest of the world after that.

VMware Cloud on AWS is vSphere, vCenter, NSX, and vSAN running ontop of AWS Elastic cloud services. Essentially, any VM that you run onprem, can be run on AWS, using VMware Cloud on AWS.

The AWS EC2 machines you run VMware on are BIG – 2 CPU, 36 cores (72 hyper threads) with 512GiB of memory and a local (SSD) cache of 3.6TB/10.7TB raw capacity. VMware Cloud on AWS requires four EC2 instances to run. No information about the networking capabilities but I assume HIGH SPEED.

The cost for the service is high but you are paying for 7x24x365 AWS EC2 services. For a 3 year “reservation”, it will cost $109.4K/host. That comes out to be about $3K/month/host for 36 months. VMware claims that on a 3 year TCO basis this would be cheaper than running an equivalent configuration onprem.

You can also contract for VMware Cloud on AWS on an hourly basis. You do have to have a VMware login and VMware credits (?) to do so. It’s certainly not as simple as just having a credit card and an AWS login. But the costs for this are $8.361/hour/host. This seems awfully high but there’s no direct comparison to other EC2 machine configurations. Although there is an EC2 X1.16 with 64 vCPUs (hyper thread equivalents), 976GiB DRAM and 1-1920 (GiB) SSD that lists for $6.669/hour – close, but not a complete match.

You are running a VMware service on AWS so the billing is done through VMware. And any data you move in or out of the cloud will be billed (through VMware) at whatever AWS would charge for the data egress/import.

It seems that if you “connect” your VMware Cloud on AWS to your onprem   vSphere cluster (through stretched layer 2 NSX networking and ? other means) you can vMotion VMs from onprem to AWS and back again. There is a behind the scenes Storage vMotion that also happens to get the data to AWS so that the VMs can operate properly.

VMware vCenter offers a dashboard of sorts to tell admins whether a particular VM is a good candidate to move to AWS or not. This is based on the VM’s connections to other VMs and maybe the amount of data that would need to moved.


Next, (PKS) containers and more (GCP) cloud

VMware together with Pivotal and Google Cloud announced a tech preview of the Pivotal Container Service (PKS) on vSphere. The new service implements Pivotal Kubo, or Kubernetes container orchestration with Bosh HA infrastructure management ontop of vSphere. PKS also comes with Harbor a secure, enterprise class container registry from VMware

This would allow a development team to develop a container micro-services application, completely within a VMware environment and to run it under vSphere. This seems tailor made to cloud developers.

Kubernetes has worker and master nodes and each which would run as a VM on vSphere. Inside worker nodes, Kubernetes runs Pods which have one or more tightly connected container(s) which enclose an application and share context.

I was talking with the vSphere team and they had been spending a lot of time making vSphere native services available to PKS. This means that you can use NSX networking and vSAN, VVOLs or VMDK storage for your container (persistent) storage.

Not exactly sure where DevOps fits into PKS on vSphere but my assumption is that you could run PuppetChef or if your up to the challenge, vRA to automate application roll out.

There was specific talk of having PKS run on AWS, probably within VMware Cloud on AWS in the future.

Of course, PKS containers that run on vSphere are completely compatible with GKE (Google Container Engine) which runs on Google Cloud Platform

No information on VMware PKS pricing as of yet.

Where lies Photon and VIC (VMware Integrated Containers)

You may recall that VMware announced Photon last year which was a open source container framework and Photon OS which was an OS for Photon containers. This still exists as an open source project and is still being developed but there was nary a word about Photon this year.

VIC still exists. VIC can support running a container as a VM but is not a real container orchestration engine. Yes you could potentially run Docker Swarm as VM or a number of containers as separate VMs under VI, but this is not the same as having a fully integrated container orchestration and management service layer in vSphere. That’s where PKS fits in.

~~~~

Although timelines weren’t discussed there were a number of discussions that led me to believe that VMware on AWS would be rolled out to other public cloud provider (read Azure and GCP). And how long it would take to be rolled out to other AWS regions around the world was not discussed.  VMware Cloud would really make sense to run on GCP, but Azure might be a bit of a stretch.

Similarly, PKS seems already heading for VMware Cloud on AWS and is already available in native form as GKE on GCP. But Azure already has a native Kubernetes Container Service. And there was no discussion as to whether PKS would be made available on IBM Softlayer or OVH vCloud Air.

Stay tuned more to come as VMware finds its true path to the cloud.

Research reveals ~liquid nitrogen temperature molecular magnets with 100X denser storage


Must be on a materials science binge these days. I read another article this week in Phys.org on “Major leap towards data storage at the molecular level” reporting on a Nature article “Molecular magnetic hysteresis at 60K“, where researchers from University of Manchester, led by Dr David Mills and Dr Nicholas Chilton from the School of Chemistry, have come up with a new material that provides molecular level magnetics at almost liquid nitrogen temperatures.

Previously, molecular magnets only operated at from 4 to 14K (degrees Kelvin) from research done over the last 25 years or so, but this new  research shows similar effects operating at ~60K or close to liquid nitrogen temperatures. Nitrogen freezes at 63K and boils at ~77K, and I would guess, is liquid somewhere between those temperatures.

What new material

The new material, “hexa-tert-butyldysprosocenium complex—[Dy(Cpttt)2][B(C6F5)4], with Cpttt = {C5H2tBu3-1,2,4} and tBu = C(CH3)3“, dysprosocenium for short was designed (?) by the researchers at Manchester and was shown to exhibit magnetism at the molecular level at 60K.

The storage effect is hysteresis, which is a materials ability to remember the last (magnetic/electrical/?) field it was exposed to and the magnetic field is measured in oersteds.

The researchers claim the new material provides magnetic hysteresis at a sweep level of 22 oersteds. Not sure what “sweep level of 22 oersteds” means but I assume a molecule of the material is magnetized with a field strength of 22 oersteds and retains this magnetic field over time.

Reports of disk’s death, have been greatly exaggerated

While there seems to be no end in sight for the densities of flash storage these days with 3D NAND (see my 3D NAND, how high can it go post or listen to our GBoS FMS2017 wrap-up with Jim Handy podcast), the disk industry lives on.

Disk industry researchers have been investigating HAMR, ([laser] heat assisted magnetic recording, see my Disk density hits new record … post) for some time now to increase disk storage density. But to my knowledge HAMR has not come out in any generally available disk device on the market yet. HAMR was supposed to provide the next big increase in disk storage densities.

Maybe they should be looking at CAMMR, or cold assisted magnetic molecular recording (heard it here, 1st).

According to Dr Chilton using the new material at 60K in a disk device would increase capacity by 100X. Western Digital just announced a 20TB MyBook Duo disk system for desktop storage and backup. With this new material, at 100X current densities, we could have 2PB Mybook Duo storage system on your desktop.

That should keep my ever increasing video-photo-music library in fine shape and everything else backed up for a little while longer.

Comments?

Photo Credit(s): Molecular magnetic hysteresis at 60K, Nature article

 

Materials science rescues civilization, again

Read a bunch of articles this past week from MIT Technology Review, How materials science will determine the future of human civilization, from Stanford University, New ultra thin semiconductor materials…, and Wired, This battery breakthrough could change everything.

The message varied a bit between articles but there was an underlying theme to all of them. Materials science was taking off, unlike it ever has before. Let’s take them on, one by one, last in first out.

New battery materials

I have not reported on new battery structures or materials in the past but it seems that every week or so I run across another article or two on the latest battery technology that will change everything. Yet this one just might do that.

I am no material scientist but Bill Joy has been investing in a company, Ionic Materials, for a while now (both in his job as a VC partner and as in independent invested) that has been working on a solid battery material that could be used to create rechargeable batteries.

The problems with Li(thium)-Ion batteries today are that they are a safety risk (lithium is a highly flammable liquid) and they use an awful lot of a relatively scarce mineral (lithium is mined in Chile, Argentina, Australia, China and other countries with little mined in USA). Electric cars would not be possible today with Li-On batteries.

Ionic Materials claim to have designed a solid polymer electrolyte that can combine the properties of familiar, ultra-safe alkaline batteries we use everyday and the recharge ability of  Li-Ion batteries used in phones and cars today. This would make a cheap, safe rechargeable battery that could work anywhere. The polymer just happens to also be fire retardant.

The historic problems with alkaline, essentially zinc and manganese dioxide is that they can’t be recharged too many times before they short out. But with the new polymer these batteries could essentially be recharged for as many times as Li-Ion today.

Currently, the new material doesn’t have as many recharge cycles as they want but they are working on it. Joy calls the material ional.

New semiconductor materials

Moore’s law will eventually cease. It’s only a question of time and materials.

Silicon is increasingly looking old in the tooth. As researchers shrink silicon devices down to atomic scales, they start to breakdown and stop functioning.

The advantages of silicon are that it is extremely scaleable (shrinkable) and easy to rust. Silicon rust or silicon dioxide was very important because it is used as an insulator. As an insulating layer, it could be patterned just like the silicon circuits themselves. That way everything (circuits, gates, switches and insulators) could all use the same, elemental material.

A couple of Stanford researchers, Eric Pop and Michal Mleczko, a electrical engineering professor and a post doc researcher, have discovered two new materials that may just take Moore’s law into a couple of more chip generations. They wrote about these new materials in their paper in Science Advances.

The new materials: hafnium diselenide and zirconium diselenide have many similar properties to silicon. One is that they can be easily made to scale. But devices made with the new materials still function at smaller geometries, at just three atoms thick (0.67nm) and also consume happen less power.

That’s good but they also rust better. When the new materials rust, they form a high-K insulating material. With silicon, high-K insulators required additional materials/processing and more than just simple silicon rust anymore. And the new materials also match Silicon’s band gap.

Apparently the next step with these new materials is to create electrical contacts. And I am sure as any new material, introduced to chip fabrication will take quite awhile to solver all the technical hurdles. But it’s comforting to know that Moore’s law will be around another decade or two to keep us humming away.

New multiferric materials

But just maybe the endgame in chip fabrication materials and possibly many other domains seems to be new materials coming out of ETH Zurich Switzerland.

There a researcher, Nicola Saldi,n has described a new sort of material that has both ferro-electric and ferro-magnetic properties.

Spaldin starts her paper off by discussing how civilization evolved mainly due to materials science.

Way in the past, fibers and rosin allowed humans to attach stone blades and other material to poles/arrows/axhandles to hunt  and farm better. Later, the discovery of smelting and basic metallurgy led to the casting of bronze in the bronze age and later iron, that could also be hammered, led to the iron age.  The discovery of the electron led to the vacuum tube. Pure silicon came out during World War II and led to silicon transistors and the chip fabrication technology we have today

Spaldin talks about the other major problem with silicon, it consumes lots of energy. At current trends, almost half of all worldwide energy production will be used to power silicon electronics in a couple of decades.

Spaldin’s solution to the  energy consumption problem is multiferric materials. These materials offer both ferro-electric and ferro-magnetic properties in the same materials.

Historically, materials were either ferro-electric or ferro-magnetic but never both. However, Spaldin discovered there was nothing in nature prohibiting the two from co-existing in the same material. Then she and her compatriots designed new multiferric materials that could do just that.

As I understand it, ferro-electric material allow electrons to form chemical structures which create electrical dipoles or electronic fields. Similarly, ferro-magnetic materials allow chemical structures to create magnetic dipoles or magnetic fields.

That is multiferric materials can be used to create both magnetic and electronic fields. And the surprising part was that the boundaries between multiferric magnetic fields (domains) form nano-scale, conducting channels which can be moved around using electrical fields.

Seems to me that if this were all possible and one could fabricate a substrate using multi-ferrics and write (program) any electronic circuit  you want just by creating a precise magnetic and electrical field ontop of it. And with todays disk and tape devices, precise magnetic fields are readily available for circular and linear materials. And it would seem just as easy to use multi multiferric material for persistent data storage.

Spaldin goes on to say that replacing magnetic fields in todays magnetism centric information/storage industry with electrical fields should lead to  reduced energy consumption.

Welcome to the Multiferric age.

Photo Credit(s): Battery Recycling by Heather Kennedy;

AMD Quad Core backside by Don Scansen;  and

Magnetic Field – 14 by Windell Oskay

Industrial revolutions, deep learning & NVIDIA’s 3U AI super computer @ FMS 2017

I was at Flash Memory Summit this past week and besides the fire on the exhibit floor, there was a interesting keynote by Andy Steinbach, PhD from NVIDIA on “Deep Learning: Extracting Maximum Knowledge from Big Data using Big Compute”.  The title was a bit much but his session was great.

2012 the dawn of the 4th industrial revolution

Steinbach started off describing AI, machine learning and deep learning as another industrial revolution, similar to the emergence of steam engines, mass production and automation of production. All of which have changed the world for the better.

Steinbach said that AI is been gestating for 50 years now but in 2012 there was a step change in it’s capabilities.

Prior to 2012 hand coded AI image recognition algorithms were able to achieve about a 74%  image recognition level but in 2012, a deep learning algorithm achieved almost 85%, in one year.

And since then it’s been on a linear trend of improvements such that in 2015, current deep learning algorithms are better than human image recognition. Similar step function improvements were seen in speech recognition as well around 2012.

What drove the improvement?

Machine and deep learning depend on convolutional neural networks. These are layers of connected nodes. There are typically an input layer and output layer and N number of internal layers in a network. The connection weights between nodes control the response of the network.

Todays image recognition convolutional networks can have ~10 layers, billions of parameters, take ~30 Exaflops to train, using 10M images and took days to weeks to train.

Image recognition covolutional neural networks end up modeling the human visual cortex which has neurons to recognize edges and other specialized characteristics of a visual field.

The other thing that happened was that convolutional neural nets were translated to execute on GPUs in 2011. Neural networks had been around in AI since almost the very beginning but their computational complexity made them impossible to use effectively until recently. GPUs with 1000s of cores all able to double precision floating point operations made these networks now much more feasible.

Deep learning training of a network takes place through optimization of the node connections weights. This is done via a back propagation algorithm that was invented in the 1980’s.  Back propagation typically depends on “supervised learning” which adjust the weights of the connections between nodes to come closer to the correct answer, like recognizing Sarah in an image.

Deep learning today

Steinbach showed multiple examples of deep learning algorithms such as:

  • Mortgage prepayment predictor system which takes information about a mortgagee, location, and other data and predicts whether they will pre-pay their mortgage.
  • Car automation image recognition system which recognizes people, cars, lanes, road surfaces, obstacles and just about anything else in front of a car traveling a road.
  • X-ray diagnostic system that can diagnose diseases present in people from the X-ray images.

As far as I know all these algorithms use supervised learning and back propagation to train a convolutional network.

Steinbach did show an example of “un-supervised learning” which essentially was fed a bunch of images and did clustering analysis on them.  Not sure what the back propagation tried to optimize but the system was used to cluster the images in the set. It was able to identify one cluster of just military aircraft images out of the data.

The other advantage of convolutional neural networks is that they can be reused. E.g. the X-ray diagnostic system above used an image recognition neural net as a starting point and then ran it against a supervised set of X-rays with doctor provided diagnoses.

Another advantage of deep learning is that it can handle any number of dimensions. Mathematical optimization algorithms can handle a relatively few dimensions but deep learning can handle any number of dimensions.  The number of input dimensions, the number of nodes in each layer and number of layers in your network are only limited by computational power.

NVIDIA’s DGX a deep learning super computer

At the end of Stienbach’s talk he mentioned the DGX appliance designed by NVIDIA for AI research.

The appliance has 8 state of the art NVIDIA GPUs, connected over a high speed NVLink with anywhere from ~29K to ~41K cores depending on GPU selected, and is capable of 170 to 960 Flops (FP16).

Steinbach said this single 3u appliance would have been rated the number one supercomputer in 2004 beating out a building full of servers. If you were to connect 13 (I think) DGX’s together, you would qualify to be on the top 500 super computers in the world.

~~~~

Comments?

Photo credit(s): Steinbach’s “Deep Learning: Extracting Maximum Knowledge from Big Data using Big Compute” presentation at FMS 2017.

Old world AI, Checkers, and The Champion

Read an article in The Atlantic this week (How checkers was solved) on Jonathan Schaeffer, the man who solved checkers, and his quest to beat Marion Tinsley, The Champion.

But first some personal history, while I was at university (back in the early 70’s) and first learned how to code in real (Fortran, 360/Assembler, IBM PL/I, Cobol) languages, one independent project I worked on was a checkers playing program. It made use of advanced alpha-beta search optimizations, board analysis routines and move trees.

These were the days of punched card decks and JCL, submitting programs to run as a batch job and getting results hours to days later. For one semester, I won the honor of consuming the most CPU time of any person in the school. I still have the card deck someplace but it may be hard to find a card reader, let alone a PL/I compiler/DOS system to run it.

In any case, better men than I have taken up the checkers challenge over time. And Schaeffer had made it his life’s work to conquer checkers and did it with his program, Chinook.

In my day checkers was a young kid and old person game. It was simple enough to learn but devilishly hard to master. My program got to look about 3.5 moves ahead, Schaeffer’s later program, used during an early match, was looking 16 moves ahead and was improved from there.

Besting The Champion

From the 50s through the early 90s there was one man who was the undisputed Champion of Checkers and that was Tinsley. Although he lost a few games during his time to other men, he never lost a match.

The article talks about how Schaeffer improved Chinook over time and at one time it had beaten Tinsley in two games but still lost the match. With a later version, it beat Tinsley a couple of times and then Tinsley fell ill and had to leave the game, later dying and forfeiting the match.

But even after Tinsley’s death, Schaeffer kept on improving Chinook.

Early on Schaeffer had a checkers endgame database and an opening database that were computed by Chinook as optimal move sequences from valid openings (professional checkers has a set of 3 move openings that players select at random and the game takes off from there) and endgames (positions with limited number’s of pieces to the end of the game).

These opening and endgame databases were stored for later retrieval during a game. This way if a game fell into a set opening or endgame the program could just follow the optimal play that was already computed.

Solving checkers

As computing power increased, Chinook’s end game database started earlier in the game with more pieces on the board and his opening database started working towards later into the game, following opening moves farther into the mid game.

When Schaeffer’s program solved checkers, essentially his opening database and his endgame database met in the middle of the game. And at that point he had the solution to every checkers position/game that could ever be.

AI vs. humans today

AI has changed to a different way of operating over time. When I was coding my checkers program, it was search trees/optimizations and board analysis. In fact, in 1996 IBM Deep Blue used variants of these techniques to beat Garry Kasparov, then World Chess Champion.

Today’s machine learning is less about search algorithms, game analyses, and game (or logic) databases and more about neural nets, machine learning and reinforcement learning.

New AI finally conquered Go only a couple of years ago, a game that’s very much more complex than checkers or chess. But in 2017 Google (Deepmind) AlphaGo didn’t use search trees and board analyses, it used neural nets, machine learning and reinforcement learning to beat Ke Jie, the then World #1 ranked Go Master.

Welcome to the new world of AI.

Photo Credit(s):

Collaboration as a function of proximity vs. heterogeneity, MIT research

Read an article the other week in MIT news on how Proximity boosts collaboration on MIT campus. Using MIT patents and papers published between 2004-2014, researchers determined how collaboration varied based on proximity or physical distance.

What they found was that distance matters. The closer you are to a person the more likely you are collaborate with him or her (on papers and patents at least).

Paper results

In looking at the PLOS research paper (An exploration of collaborative scientific production at MIT …), one can see that the relative frequency of collaboration decays as distance increases (Graph A shows frequency of collaboration vs. proximity for papers and Graph B shows a similar relationship for patents).

 

Other paper results

The two sets of charts below show the buildings where research (papers and patents) was generated. Building heterogeneity, crowdedness (lab space/researcher) and number of papers and patents per building is displayed using the color of the building.

The number of papers and patents per building is self evident.

The heterogeneity of a building is a function of the number of different departments that use the building. The crowdedness of a building is an indication of how much lab space per faculty member a building has. So the more crowded buildings are lighter in color and less crowded buildings are darker in color.

I would like to point out Building 32. It seems to have a high heterogeneity, moderate crowdedness and a high paper production but a relatively low patent production. Conversely, Building 68 has a low heterogeneity, low crowdedness and a high production of papers and a relatively low production of patents. So similar results have been obtained from buildings that have different crowdedness and different heterogeneity.

The paper specifically cites buildings 3 & 32 as being most diverse on campus and as “hubs on campus” for research activity.  The paper states that these buildings were outliers in research production on a per person basis.

And yet there’s no global correlation between heterogeneity or crowdedness for that matter and (paper/patent) research production. I view crowdedness as a substitute for researcher proximity. That is the more crowded a building is the closer researchers should be. Such buildings should theoretically be hotbeds of collaboration. But it doesn’t seem like they have any more papers than non-crowded buildings.

Also heterogeneity is often cited as a generator of research. Steven Johnson’s Where Good Ideas Come From, frequently mentions that good research often derives from collaboration outside your area of speciality. And yet, high heterogeneity buildings don’t seem to have a high production of research, at least for patents.

So I am perplexed and unsatisfied with the research. Yes proximity leads to more collaboration but it doesn’t necessarily lead to more papers or patents. The paper shows other information on the number of papers and patents by discipline which may be confounding results in this regard.

Telecommuting and productivity

So what does this tell us about the plight of telecommuters in todays business and R&D environments. While the paper has shown that collaboration goes down as a function of distance, it doesn’t show that an increase in collaboration leads to more research or productivity.

This last chart from the paper shows how collaboration on papers is trending down and on patents is trending up. For both papers and patents, inter-departmental collaboration is more important than inter-building collaboration. Indeed, the sidebars seem to show that the MIT faculty participation in papers and patents is flat over the whole time period even though the number of authors (for papers) and inventors (for patents) is going up.

So, I,  as a one person company can be considered an extreme telecommuter for any organization I work with. I am often concerned that  my lack of proximity to others adversely limits my productivity. Thankfully the research is inconclusive at best on this and if anything tells me that this is not a significant factor in research productivity

And yet, many companies (Yahoo, IBM, and others) have recently instituted policies restricting telecommuting because, they believe,  it  reduces productivity. This research does not show that.

So IBM and Yahoo I think what you are doing to concentrate your employee population and reduce or outright eliminate telecommuting is wrong.

Picture credit(s): All charts and figures are from the PLOS paper. 

 

New chip architecture with CPU, storage & sensors in one package

Read an article the other day in MIT news, (3D chip combines computing and data storage) about a new 3D chip out of Stanford and MIT research, which includes CPU, RRAM (resistive RAM) storage class memories and sensors in one single package. Such a chip architecture vastly minimizes the off chip bottleneck to access storage and sensors.

Chip componentry

The chip’s sensors are based on carbon nanotubes. Aside from a layer of silicon at the bottom, all the rest of transistors used in the chip are also based off of carbon nanotube FET (field effect transistors).

The RRAM storage class memory is a based on a dielectric material which uses electrical resistance to store non-volatile data.

The bottom layer is a silicon based CPU. On top of the silicon is a carbon nanotube layer. Next comes the RRAM and the top layer is more carbon nanotubes making up the sensor array.

Architectural benefits

One obvious benefit is having data storage directly accessible to the CPU is that there’s no longer a need to go off chip to access data. The 2nd major advantage to the chip architecture is that the sensor array can write directly to RRAM storage, so there’s no off chip delay to provide sensor readout and storage.

Another advantage to using carbon nanotube FET’s is that they can be an order of magnitude more energy efficient than silicon transistors. Moreover, RRAM has the potential to be much denser than DRAM.

Finally, another major advantage is that this can all be built in one 3D chip because carbon nanotube and RRAM fabrication can be done at relatively cooler temperatures (~200C) vs. silicon fabrication which requires relatively high temperatures (1000C). Silicon cannot be readily fabricated in multiple layers because of the high temperatures required which will harm lower layers. But you could fabricate the lowest layer in silicon and then the rest as either carbon nanotube FETs or RRAM without harming the silicon layer.

Transistor/RRAM counts

The chip as fabricated has a million RRAM cells (bits?) and 2 million nanotube FETs. In contrast, in 2014, Intel’s 15-core Xeon Ivy Bridge EX had 4.3B transistors and current DRAM chips offer 64Gb. So there’s a ways to go before carbon nanotube and RRAM densities can get to a level available from silicon today.

However, as they have a bottom layer of silicon they can have all the CPU complexity of an Intel processor and still build RRAM and carbon nanotubes FETs on top of that. Which makes this chip architecture compatible with current CMOS fabrication techniques and a very interesting addition to current CPU architectures.

~~~~

Unclear to me why they stopped at 4 layers (1-silicon FET, 1 carbon nanotubes FET, 1 RRAM and 1 carbon nanotubes FET [sensor array]). If they can do 4 why not do 5 or more. That way they could pack in even more RRAM storage and perhaps more sensor layers.

Also, not sure what the bottom most layer of carbon nanotubes is doing. If I had to hazard a guess, it’s being used for RRAM control logic. But I could be wrong.

I could see how these chips could be used for very specialized sensor applications, with a limited need for data storage. The researchers claim many types of sensors can be created using carbon nanotubes. If that’s the case, maybe we might see these sorts of chips showing up all over the place.

Comments?

Photo Credit(s): Three dimensional integration of nanotechnologies for computing and data storage on a single chip, Nature magazine. 

Zipline delivers blood 7X24 using fixed wing drones in Rwanda

Read an article the other day in MIT Tech Review (Zipline’s ambitious medical drone delivery in Africa) about a startup in Silicon Valley, Zipline, that has started delivering blood by drones to remote medical centers in Rwanda.

We’ve talked about drones before (see my Drones as a leapfrog technology post) and how they could be another leapfrog 3rd world countries into the 21st century. Similar, to cell phones, drones could be used to advance infrastructure without having to go replicate the same paths as 1st world countries such as building roads/hiways, trains and other transport infrastructure.

The country

Rwanda is a very hilly but small (10.2K SqMi/26.3 SqKm) and populous (pop. 11.3m) country in east-central Africa, just a few degrees south of the Equator. Rwanda’s economy is based on subsistence agriculture with a growing eco-tourism segment.

Nonetheless, with all
its hills and poverty roads in Rwanda are not the best. In the past delivering blood supplies to remote health centers could often take hours or more. But with the new Zipline drone delivery service technicians can order up blood products with an app on a smart phone and have it delivered via parachute to their center within 20 minutes.

Drone delivery operations

In the nest, a center for drone operations, there is a tent housing the blood supplies, and logistics for the drone force. Beside the tent are a steel runway/catapults that can launch drones and on the other side of the tent are brown inflatable pillows  used to land the drones.

The drones take a pre-planned path to the remote health centers and drop their cargo via parachute to within a five meter diameter circle.

Operators fly the drones using an iPad and each drone has an internal navigation system. Drones fly a pre-planned flightaugmented with realtime kinematic satellite navigation. Drone travel is integrated within Rwanda’s controlled air space. Routes are pre-mapped using detailed ground surveys.

Drone delivery works

Zipline drone blood deliveries have been taking place since late 2016. Deliveries started M-F, during daylight only. But by April, they were delivering 7 days a week, day and night.

Zipline currently only operates in Rwanda and only delivers blood but they have plans to extend deliveries to other medical products and to expand beyond Rwanda.

On their website they stated that before Zipline, delivering blood to one health center would take four hours by truck which can now be done in 17 minutes. Their Muhanga drone center serves 21 medical centers throughout western Rwanda.

Photo Credits: Flyzipline.com