A new way to compute

I read an article the other day on using using random pulses rather than digital numbers to compute with, see Computing with random pulses promises to simplify circuitry and save power, in IEEE Spectrum. Essentially they encode a number as a probability in a random string of bits and then use simple logic to compute with. This approach was invented in the early days of digital logic and was called stochastic computing.

Stochastic numbers?

It’s pretty easy to understand how such logic can work for fractions. For example to represent 1/4, you would construct a bit stream that had one out of every four bits, on average, as a 1 and the rest 0’s. This could easily be a random string of bits which have an average of 1 out of every 4 bits as a one.

A nice result of such a numerical representation is that it easily results in more precision as you increase the length of the bit stream. The paper calls this progressive precision.

Progressive precision helps stochastic computing be more fault tolerant than standard digital logic. That is, if the string has one bit changed it’s not going to make that much of a difference from the original string and computing with an erroneous number like this will probably result in similar results to the correct number.  To have anything like this in digital computation requires parity bits, ECC, CRC and other error correction mechanisms and the logic required to implement these is extensive.

Stochastic computing

2 bit multiplier

Another advantage of stochastic computation and using a probability  rather than binary (or decimal) digital representation, is that most arithmetic functions are much simpler to implement.


They discuss two examples in the original paper:

  • AND gate

    Multiplication – Multiplying two probabilistic bit streams together is as simple as ANDing the two strings.

  • 2 input stream multiplexer

    Addition – Adding two probabilistic bit strings together just requires a multiplexer, but you end up with a bit string that is the sum of the two divided by two.

What about other numbers?

I see a couple of problems with stochastic computing:,

  • How do you represent  an irrational number, such as the square root of 2;
  • How do you represent integers or for that matter any value greater than 1.0 in a probabilistic bit stream; and
  • How do you represent negative values in a bit stream.

I suppose irrational numbers could be represented by taking a near-by, close approximation of the irrational number. For instance, using 1.4 for the square root of two, or 1.41, or 1.414, …. And this way you could get whatever (progressive) precision that was needed.

As for integers greater than 1.0, perhaps they could use a floating point representation, with two defined bit strings, one representing the mantissa (fractional part) and the other an exponent. We would assume that the exponent rather than being a probability from 0..1.0, would be inverted and represent 1.0…∞.

Negative numbers are a different problem. One way to supply negative numbers is to use something akin to complemetary representation. For example, rather than the probabilistic bit stream representing 0.0 to 1.0 have it represent -0.5 to 0.5. Then progressive precision would work for negative numbers as well a positive numbers.

One major downside to stochastic numbers and computation is that high precision arithmetic is very difficult to achieve.  To perform 32 bit precision arithmetic would require a bit streams that were  2³² bits long. 64 bit precision would require streams that were  2**64th bits long.

Good uses for stochastic computing

One advantage of simplified logic used in stochastic computing is it needs a lot less power to compute. One example in the paper they use for stochastic computers is as a retinal sensor for in the body visual augmentation. They developed a neural net that did edge detection that used a stochastic front end to simplify the logic and cut down on power requirements.

Other areas where stochastic computing might help is for IoT applications. There’s been a lot of interest in IoT sensors being embedded in streets, parking lots, buildings, bridges, trucks, cars etc. Most have a need to perform a modest amount of edge computing and then send information up to the cloud or some edge consolidator intermediate

Many of these embedded devices lack access to power, so they will need to make do with whatever they can find.  One approach is to siphon power from ambient radio (see this  Electricity harvesting… article), temperature differences (see this MIT … power from daily temperature swings article), footsteps (see Pavegen) or other mechanisms.

The other use for stochastic computing is to mimic the brain. It appears that the brain encodes information in pulses of electric potential. Computation in the brain happens across exhibitory and inhibitory circuits that all seem to interact together.  Stochastic computing might be an effective way, low power way to simulate the brain at a much finer granularity than what’s available today using standard digital computation.


Not sure it’s all there yet, but there’s definitely some advantages to stochastic computing. I could see it being especially useful for in body sensors and many IoT devices.


Photo Credit(s):  The logic of random pulses

2 bit by 2 bit multiplier, By Sodaboy1138 (talk) (Uploads) – Own work, CC BY-SA 3.0, wikimedia

AND ANSI Labelled, By Inductiveload – Own work, Public Domain, wikimedia

2 Input multiplexor

A battery free implantable neural sensor, MIT Technology Review article

Integrating neural signal and embedded system for controlling a small motor, an IntechOpen article

Blockchain, open source and trusted data lead to better SDG impacts

Read an article today in Bitcoin magazine IXO Foundation: A blockchain based response to UN call for [better] data which discusses how the UN can use blockchains to improve their development projects.

The UN introduced the 17 Global Goals for Sustainable Development (SDG) to be achieved in the world by 2030. The previous 8 Millennial Development Goals (MDG) expire this year.

Although significant progress has been made on the MDGs, one ongoing determent to  MDG attainment has been that progress has been very uneven, “with the poorest and economically disadvantaged often bypassed”.  (See WEF, What are Sustainable Development Goals).

Throughout the UN 17 SDG, the underlying objective is to end global poverty  in a sustainable way.

Impact claims

In the past organizations performing services for the UN under the MDG mandate, indicated they were performing work toward the goals by stating, for example, that they planted 1K acres of trees, taught 2K underage children or distributed 20 tons of food aid.

The problem with such organizational claims is they were left mostly unverified. So the UN, NGOs and other charities funding these projects were dependent on trusting the delivering organization to tell the truth about what they were doing on the ground.

However, impact claims such as these can be independently validated and by doing so the UN and other funding agencies can determine if their money is being spent properly.

Proving impact

Proofs of Impact Claims can be done by an automated bot, an independent evaluator or some combination of the two . For instance, a bot could be used to analyze periodic satellite imagery to determine whether 1K acres of trees were actually planted or not; an independent evaluator can determine if 2K students are attending class or not, and both bots and evaluators can determine if 20 tons of food aid has been distributed or not.

Such Proofs of Impact Claims then become a important check on what organizations performing services are actually doing.  With over $1T spent every year on UN’s SDG activities, understanding which organizations actually perform the work and which don’t is a major step towards optimizing the SDG process. But for Impact Claims and Proofs of Impact Claims to provide such feedback but they must be adequately traced back to identified parties, certified as trustworthy and be widely available.

The ixo Foundation

The ixo Foundation is using open source, smart contract blockchains, personalized data privacy, and other technologies in the ixo Protocol for UN and other organizations to use to manage and provide trustworthy data on SDG projects from start to completion.

Trustworthy data seems a great application for blockchain technology. Blockchains have a number of features used to create trusted data:

  1. Any impact claim and proofs of impacts become inherently immutable, once entered into a blockchain.
  2. All parties to a project, funders, services and evaluators can be clearly identified and traced using the blockchain public key infrastructure.
  3. Any data can be stored in a blockchain. So, any satellite imagery used, the automated analysis bot/program used, as well as any derived analysis result could all be stored in an intelligent blockchain.
  4. Blockchain data is inherently widely available and distributed, in fact, blockchain data needs to be widely distributed in order to work properly.


The ixo Protocol

The ixo Protocol is a method to manage (SDG) Impact projects. It starts with 3 main participants: funding agencies, service agents and evaluation agents.

  • Funding agencies create and digitally sign new Impact Projects with pre-defined criteria to identify appropriate service  agencies which can do the work of the project and evaluation agencies which can evaluate the work being performed. Funding agencies also identify Impact Claim Template(s) for the project which identify standard ways to assess whether the project is being performed properly used by service agencies doing the work. Funding agencies also specify the evaluation criteria used by evaluation agencies to validate claims.
  • Service agencies select among the open Impact Projects whichever ones they want to perform.  As the service agencies perform the work, impact claims are created according to templates defined by funders, digitally signed, recorded and collected into an Impact Claim Set underthe IXO protocol.  For example Impact Claims could be barcode scans off of food being distributed which are digitally signed by the servicing agent and agency. Impact claims can be constructed to not hold personal identification data but still cryptographically identify the appropriate parties performing the work.
  • Evaluation agencies then take the impact claim set and perform the  evaluation process as specified by funding agencies. The evaluation insures that the Impact Claims reflect that the work is being done correctly and that the Impact Project is being executed properly. Impact claim evaluations are also digitally signed by the evaluation agency and agent(s), recorded and widely distributed.

The Impact Project definition, Impact Claim Templates, Impact Claim sets, Impact Claim Evaluations are all available worldwide, in an Global Impact Ledger and accessible to any and all funding agencies, service agencies and evaluation agencies.  At project completion, funding agencies should now have a granular record of all claims made by service agency’s agents for the project and what the evaluation agency says was actually done or not.

Such information can then be used to guide the next round of Impact Project awards to further advance the UN SDGs.

Ambly project

The Ambly Project is using the ixo Protocol to supply childhood education to underprivileged children in South Africa.

It combines mobile apps with blockchain smart contracts to replace an existing paper based school attendance system.

The mobile app is used to record attendance each day which creates an impact claim which can then be validated by evaluators to insure children are being educated and properly attending class.


Blockchains have the potential to revolutionize financial services, provide supply chain provenance (e.g., diamonds with Blockchains at IBM), validate company to company contracts (Ethereum enters the enterprise) and now improve UN SDG attainment.

Welcome to the new blockchain world.

Photo Credit(s): What are Sustainable Development Goals, World Economic Forum;

IXO Foundation website

Ambly Project webpage

Mobile devices as a cache for cloud data

Howard Marks (DeepStorage.net) and I were on a GreyBeard’s podcast last month (PB are the new TB) talking with the CTO (Brian Carmody [@initzero]) of hybrid storage vendor, Infinidat, who just happened to mention in passing that “our mobile devices pretty much act as caches for cloud data”. That’s interesting.

Mobile app’s caching data

There’s a part of me that couldn’t agree more. Most of my mobile mail uses IMAP which acts as a browser for email residing elsewhere. Radio apps stream music from the cloud. Photo apps can store pictures on the cloud. Social media apps (Facebook, LinkedIN, Twitter, etc.) use the cloud to store posts/pokes/photos and only cache minimal data locally. There are many more apps that act similarly.

But not all data is cached

On the other hand, I have downloaded all of my music library to my mobile devices. There was a time when I was more selective but later generation devices have more than enough storage to hold it all.

Movies are another.  Most purchased movies are download to my desktop. With only 64GBs of storage on my iPad/iPhone, I have to be a bit more judicious with which movies I store on the devices. Most of the time, when I am watching movies on mobile devices, I don’t have Internet access, so caching/streaming won’t work. Yet, for some services (Amazon Prime Video & Apple TV) I do stream at home and then the TV or AppleTV caches cloud media.

My photo library is similar, there’s just too many photos to fit them all on the  device. So for now, they reside on my desktop, only a select subset are copied to the device.

Contacts, passwords, calendars and countless other datums that reside on the cloud or my desktop computer are also replicated (not cached) on mobile devices. Could they be cached, probably, but with the need for these items, even when internet service is not available, caching them makes no sense.

Storage caching vs. mobile device caching

Storage caches are pretty sophisticated and Infinidat’s as sophisticated as any of them. Historically, storage caching is resilient in the face of power outages, storage device failures, software bugs, etc. Essentially, when storage data hits the cache the storage system “guarantees” to write it to backend storage, some time in the future. Read data caching requires less resilience/fault tolerance because data already resides somewhere else on backend storage.

It’s unclear whether mobile caching has similar strengths. As each app caches data in it’s own way, there would be less resilience in mobile caching than storages subsystem caches. But I am no app developer, so don’t have a clue as to what caching services are available within the mobile app ecosystem.

Device internet speed too slow

One thing that keeps me storing data on my mobile devices is the speed of Wi-Fi and cellular internet. It’s often much slower than I would like. I suppose when these speed up there would be less need to save data on my mobile devices. But by that time, mobile storage will bemuch cheaper as well and I will have even more data to cache/store. So who knows.

Then again in the foreseeable future, there will be times without cellular or Wi-Fi Internet. So , storing data on the mobile device will always be the way to go at least for some data.

Maybe Brian’s right

From my perspective, Brian is partially right about the mobile devices caching cloud data. But maybe it’s just because I am old school that I decide to store a lot of data on my mobile devices.

From Brian’s perspective, all that data is stored elsewhere (desktop or cloud). So it all could be cached and probably should be.

As the world rolls out IoT, with even less storage at the edge, caching cloud data will become even more of a necessity. Hopefully by then Internet access will become even more universal than it is already.


Photo Credits: Blake Patterson, iPhone apps sphere

At Scale conference keynote, Facebook video experience re-engineered

11990439_1644273839179047_2244380699715442158_nThe At Scale conference happened this past week in LA. Jay Parikh, Global Head of Engineering and Infrastructure at Facebook, kicked off the conference by talking about how Facebook is attempting to conquer some of it’s intrinsic problems, as it scales up from over 1B users today. I was unable to attend the conference but watched a video of the keynote (on Facebook of course).

The At Scale community is a group of large, hyper-scale, web companies such as Google, Microsoft, Twitter, and of course Facebook, among a gaggle of others that all have problems trying to scale up their infrastructure to handle more and more users activities. They had 1800 people registered for the At Scale 2015 conference on Monday, double last years count. The At Scale community are trying to push the innovation level of the industry faster, through a community of companies that need to work at hyper-scale.

Facebook’s video problem

At Facebook the current hot problem that’s impacting customer satisfaction seems to be video uploads and playback (downloads). The issues with Facebook’s video experience are multifaceted and range from the time it takes to successfully upload a video, to the bandwidth it takes to playback a video to the -system requirements to support live streaming video to 100,000s of users.

Facebook started as a text only service, migrated to a photo oriented service, but now is quickly moving to a video oriented user experience. But it doesn’t stop there they can see on the horizon that augmented and virtual reality will become a significant driver of activity for Facebook uses?!

Daily video 1B last year now at 4B video views/day. They also launched a new service lately, LiveMentions, which was a live streaming service for celebrities (real time video streams). Several celebrities were live streaming to 150K of their subscribers. So video has become and will continue as the main consumer of bandwidth at Facebook.

Struggling to enhance the Facebook user’s video experience over the past year, they have come up with three key engineering principles that have helped them: Planning, Iteration and Performance.


Facebook is already operating a terabit scale network, so doing something to its network wrong is going to cause major problems, around the world. As a result, Facebook engineering focused early on, into incorporating lots of instrumentation in their network and infrastructure services. This has allowed them to constantly monitor the activity of their users across their infrastructure to identify problems and solutions.

One metric Parikh talked about was “playback success rate”, this is the percentage where the video starts to play in under 1 second for a facebook user.  One chart he showed, was a playback success rate colored ove a world map  but aggregated (averaged) at the country level. But with their instrumentation Facebook was able to drill down to regions within a country and  even cities within a region. This allows engineering to identify problems at almost any level of granularity they need.

One key take away to Planing, is if you have the instrumentation in place, have people to monitor and mine the data and are willing to address the problems that crop up, then you can create a more flexible, efficient and effective environment and build a better product for your users.


Iteration is not just about feature deployment, but it’s also about the Facebook user experience. Their instrumentation had told them that they were doing ok on video uploads but it turns out that when they looked at the details, they saw that some customers were not having a satisfactory video upload experience. For instance, one Facebook engineer had to wait 82 hours to upload a video.

The Facebook world is populated with 10s of thousands of unique devices with different memory, compute and storage. They had to devise approaches that could optimize the encoding for all the different devices, some of which was done on mobile phones.

They also had to try to optimize the network stack for different devices and mobile networking technologies. Parikh had another map showing network connectivity. Surprise, most of the world is not on LTE, and a vast majority of world is on 2G and 3G cellular networks. So via iteration Facebook went about improving video upload by 1% here and 1% there, but with Facebook’s user base, these improvements impact millions of users. They used cross functional teams to address the problems they uncovered.

However, video uploads problems were not just in device and connectivity realms. Turns out they had a big cancel upload button on their screen after the start of the video upload. This was sometimes clicked by mistake and they found that almost 10% of users hit the cancel upload. So they went through and re-examined the whole user experience to try to eliminate other hindrances to successful video uploads.


The key take away from this segment of the talk was that performance has to be considered from the get go of a new service or service upgrade. It is impossible to improve performance after the fact, especially for At Scale environments.

In my CS classes, the view was make it work and then make it work fast.  What Facebook has found is that you never have the time after a product has shipped to make it fast. As soon as it works, they had to move on to the next problem.

As a result if performance is not built in from the start, not a critical requirement/feature of a system architecture and design, it never gets addressed. Also if all you focus on is making it work then the design and all the code is built around feature functionality. Changing working functionality later to improve performance is an impossible task and typically represents a re-architecture/re-design/re-implementation of the functionality.

For instance, Facebook used to do video encoding in serial on a single server. It often took a long time (10 to 30 minutes). Engineering reimplemented their video encoding to partition the video and distribute the encoding across multiple servers. Doing this, sped up encoding time considerably.

But they didn’t stop there, with such a diverse user networking environment, they felt that they could save bandwidth and better optimize user playback if could reduce playback video size. They were able to take their machine learning/AI investments that Facebook has made and apply this to distributed video encoding. They were able to analyze the video scene by scene and opportunistically reduce bandwidth load and storage size but still maintain video  playback quality. By implementing the new video encoding process they have achieved double digit reductions in bandwidth requirements for playback.

Another example of the importance of performance was the LiveMentions feature discussed above. Celebrities often record streams in places with poor networking infrastructure. So in order to insure a good streaming experience Facebook  had to implement variable bit rate video upload to adjust upload bandwidth requirements based on networking environmentr. Moreover, once a celebrity starts a live stream all the fans in the world get notified. then there’s a thundering herd (boot storms anyone) to start watching the video stream. In order to support this mass streaming, Facebook implemented stream blocking, which holds off the start of a live stream viewing until they have cached enough of the video stream at their edge servers, worldwide. This guaranteed that all the fans had a good viewing experience, once it started.

There were a couple more videos of the show sessions but I didn’t have time to review them.  But Facebook sounds like a fun place to work, especially for infrastructure performance experts.



#VMworld2015 day 1 announcements


IMG_5411It seemed like today was all about the cloud and cloud native apps. Among the many announcements, VMware announced two key new capabilities: VMware integrated containers and the Python Photon Platform.

Containers running on VMware

  • VMware vSphere Integrated Containers is an implementation of containers that runs natively under vSphere. The advantage of this solution is that now when developers fire up a multi-container app,  each container now exists as a separate VM under vSphere and can be managed, monitored and secured just like any other VM in the environment. Previously a multi-container app would be one VM per container engine  containing potentially many containers running under the single VM. But with vSphere Integrated Containers, the container engine and the light weight Linux kernel (Python Photon OS) are now integrated into the ESX hypervisor so each container runs as a native VM. Integrated containers is an follow on to a combination of Project Bonneville, Project Python Photon (OS) and Instant clones. Recall with Instant Clones one can spin up a clone of a VM in less than a second and its memory footprint is 0MB.
  • Python Photon Platform takes container execution to a whole new level, with a new deployment of a hypervisor tailor made to run containers (not VMs). With the Python Photon Platform one natively runs container frameworks underneath the platform. Python Photon Platform consists of Python Photon Machine which is Python Photon OS (lightweight Linux Kernel distro) & the new Microvisor (new light weight hypervisor for container hardware calls) and Python Photon Controller which is a distributed control plane and management API. With Python Photon Platform one can manage 100K to Millions of containers, running under 1000s of container frameworks.

Over time Python Photon Platform is intended to be open sourced. VMware also announced a bundling of Pivotal Cloud Foundry with the Python Photon Platform so as to better run cloud native apps implemented in Cloud Foundry. But the ultimate intent is to provide support for Google Kubernetes, Apache Mesos and any other container framework that comes out.

So now you can run your Docker container apps or any other container app solution in two different ways. One depends on vSphere standard management platform and runs container apps as a standard VMs. The other takes a completely green field approach and runs container frameworks natively in a ground up new hypervisor solution with a new management solution altogether that scales.

The advantage of Python Photon is that it scales to extreme, cloud level types of application environments. Python Photon is intended to run cloud-native apps.

vCloud Air extensions

One of the other major things that VMware demoed today was moving a VM from on premises to vCloud Air and back again – a real crowd pleaser. One VMware Exec said that after MIT had convinced them they needed to be able to move apps from on premises to the cloud for dev-test apps. They then turned around and decided they wanted to move dev-test activity back to their onprem environment and instead wanted to move their production to vCloud Air.

They demoed both capabilities using vMotion to move a VM to vCloud Air and using it again to move it back. The nice thing about all this is that all the security and other attributes of the VM can move to the cloud and back again along with the VM. All the while the VM continued to operate, with no disruption to execution. They mention that it could potentially take hours to move the data for the VM.

IMG_5413There were a number of other capabilities announced today including EVO SDDC (EVO: RACK reborn) which includes a new datacenter management solution. Customers can now roll in a rack of servers and have EVO SDDC manage them and deploy software defined data center on them in a matter of hours. Within EVO SDDC you can have application domains which span racks of servers but provide isolation and management multi-tennancy.

NSX 6.2 was also discussed and essentially is key to extending your networking from on premises to vCloud Air. With NSX 6.2 local routing, micro segmentation security and app firewalls can be configured locally and then be “extended” to the vCloud Air environment.

Lots of moving parts here and I probably missed some key components to these solutions and didn’t cover any of them well enough other than to give a feel for what they are.

But one thing is clear, VMware’s long term strategy is to take your native, on premises VMs to vCloud Air and back again as well as if your Dev-Ops group or any other BU wants to use containers to implement cloud apps, VMware has you covered coming and going.


Flash’s only at 5% of data storage

7707062406_6508dba2a4_oWe have been hearing for years that NAND flash is at price parity with disk. But at this week’s Flash Memory Summit, Darren Thomas, VP Storage BU, Micron said at his keynote that NAND only store 5% of the bits in a data center.

Darren’s session was all about how to get flash to become more than 5% of data storage and called this “crossing the chasm”. I assume the 5% is against yearly data storage shipped.

Flash’s adoption rate

Darren, said last year flash climbed from 4% to 5% of data center storage, but he made no mention on whether flash’s adoption was accelerating. According to another of Darren’s charts, flash is expected to ship ~77B Gb of storage in 2015 and should grow to about 240B Gb by 2019.

If the ratio of flash bits shipped to data centers (vs. all flash bits shipped) holds constant then Flash should be ~15% of data storage by 2019. But this assumes data storage doesn’t grow. If we assume a 10% Y/Y CAGR for data storage, then flash would represent about ~9% of overall data storage.

Data growth at 10% could be conservative. A 2012 EE Times article said2010-2015 data growth CAGR would be 32%  and IDC’s 2012 digital universe report said that between 2012 and 2020, data will double every two years, a ~44% CAGR. But both numbers could be talking about the world’s data growth, not just data center.

How to cross this chasm?

Geoffrey Moore, author of Crossing the Chasm, came up on stage as Darren discussed what he thought it would take to go beyond early adopters (visionaries) to early majority (pragmatists) and reach wider flash adoption in data center storage. (See Wikipedia article for a summary on Crossing the Chasm.)

As one example of crossing the chasm, Darren talked about the electric light bulb. At introduction it competed against candles, oil lamps, gas lamps, etc. But it was the most expensive lighting system at the time.

But when people realized that electric lights could allow you to do stuff at night and not just go to sleep, adoption took off. At that time competitors to electric bulb did provide lighting it just wasn’t that good and in fact, most people went to bed to sleep at night because the light then available was so poor.

However, the electric bulb  higher performing lighting solution opened up the night to other activities.

What needs to change in NAND flash marketing?

From Darren’s perspective the problem with flash today is that marketing and sales of flash storage are all about speed, feeds and relative pricing against disk storage. But what’s needed is to discuss the disruptive benefits of flash/NAND storage that are impossible to achieve with disk today.

What are the disruptive benefits of NAND/flash storage,  unrealizable with disk today.

  1. Real time analytics and other RT applications;
  2. More responsive mobile and data center applications;
  3. Greener, quieter, and potentially denser data center;
  4. Storage for mobile, IoT and other ruggedized application environments.

Only the first three above apply  to data centers. And none seem as significant  as opening up the night, but maybe I am missing a few.

Also the Wikipedia article cited above states that a Crossing the Chasm approach works best for disruptive or discontinuous innovations and that more continuous innovations (doesn’t cause significant behavioral change) does better with Everett Roger’s standard diffusion of innovation approaches (see Wikepedia article for more).

So is NAND flash a disruptive or continuous innovation?  Darren seems firmly in the disruptive camp today.


Photo Credit(s): 20-nanometer NAND flash chip, IntelFreePress’ photostream

Nanterro emerges from stealth with CNT based NRAM

512px-Types_of_Carbon_NanotubesNanterro just came out of stealth this week and bagged $31.5M in a Series E funding round. Apparently, Nanterro has been developing a new form of non-volatile RAM (NRAM), based on Carbon Nanotubes (CNT), which seems to work like an old T-bar switch, only in the NM sphere and using CNT for the wiring.

They were founded in 2001, and are finally  ready to emerge from stealth. Nanterro already has 175+ issued patents, with another 200 patents pending. The NRAM is currently in production at 7 CMOS fabs already and they are sampling 4Mb NRAM chips  to a number of customers.


Performance of the NRAM is on a par with DRAM (~100 times faster than NAND), can be configured in 3D and supports MLC (multi-bits per cell) configurations.  NRAM also supports orders of magnitude more (assume they mean writes) accesses and stores data much longer than NAND.

The only question is the capacity, with shipping NAND on the order of 200Gb, NRAM is  about 2**14X behind NAND. Nanterre claims that their CNT-NRAM CMOS process can be scaled down to <5nm. Which is one or two generations below the current NAND scale factor and assuming they can pack as many bits in the same area, should be able to compete well with NAND.They claim that their NRAM technology is capable of Terabit capacities (assumed to be at the 5nm node).

The other nice thing is that Nanterro says the new NRAM uses less power than DRAM, which means that in addition to attaining higher capacities, DRAM like access times, it will also reduce power consumption.

It seems a natural for mobile applications. The press release claims it was already tested in space and there are customers looking at the technology for automobiles. The company claims the total addressable market is ~$170B USD. Which probably includes DRAM and NAND together.

CNT in CMOS chips?

Key to Nanterro’s technology was incorporating the use of CNT in CMOS processes, so that chips can be manufactured on current fab lines. It’s probably just the start of the use of CNT in electronic chips but it’s one that could potentially pay for the technology development many times over. CNT has a number of characteristics which would be beneficial to other electronic circuitry beyond NRAM.

How quickly they can ramp the capacity up from 4Mb seems to be a significant factor. Which is no doubt, why they went out for Series E funding.

So we have another new non-volatile memory technology.On the other hand, these guys seem to be a long ways away from the lab, with something that works today and the potential to go all the way down to 5nm.

It should interesting as the other NV technologies start to emerge to see which one generates sufficient market traction to succeed in the long run. Especially as NAND doesn’t seem to be slowing down much.


Picture Credits: Wikimedia.com

EMCWorld2015 day 1 news

We are at EMCWorld2015 in Vegas this week. Day 1 was great with new XtremIO 4.0, “The Beast”, new enhanced Data Protection, and a new VCE VxRACK converged infrastructure solution announcements. Somewhere in all the hoopla I saw an all flash VNXe appliance and VMAX3 with a cloud storage tier but these seemed to be just teasers.

XtremIO 4.0

The new hardware provides 40TB per X-brick and with compression/dedupe and the new 8-Xbrick cluster provides 320TB raw or 1.9PB effective capacity. As XtremIO supports 150K mixed IOPS/XBrick, an 8-Xbrick cluster could do 1.2M IOPS or with 250K read IOPS/Xbrick that’s 2.0M IOPS.

XtremIO 4.0 now also includes RecoverPoint integration. (I assume this means they have integrated the write splitter directly into XtremIO that way you don’t need the host version or the switch version of the write splitter.)

The other thing XtremIO 4.0 introduces is non-disruptive upgrades. This means that they can expand or contract the cluster without taking down IO activity.

There was also some mention of better application consistent snapshots, which I suspect means Microsoft VSS integration.

XtremIO 4.0 is a free software upgrade, so the ability to scale up to 8-Xbricks and non-disruptive cluster changes, and RecoverPoint integration can all be added to current XtremIO systems.

Data Protection

EMC introduced a new top end DataDomain hardware appliance the DataDomain 9500, which has 1.5X the performance (58.7TB/hr) and 4X the capacity (1.7PB) of their nearest competitor solution.

They also added a new software feature (from Maginetics) called CloudBoost™.  CloudBoost allows Networker and Avamar to backup to cloud storage. EMC also added Microsoft Ofc365 cloud backup to Spannings previous Google Apps and SalesForce cloud backups.

VMAX3 Protect Point was also enhanced to provide native backup for Oracle, Microsoft SQL Server, and IBM DB2 application environments. ProtectPoint offers a direct path between VMAX3 and  DataDomain appliances and can speed up backup performance by 20X.

EMC also announced Project Falcon which is a virtual appliance version of DataDomain software


This is a rack sized, stack of VSPEX Blue appliances (a VMware EVO:RAIL solution) with new software to bring the VCE useability and data center scale services to a hyper-converged solution. Each appliance is a 2U rack mounted compute intensive or storage intensive unit. The Blue appliances are configureed in a rack for VxRACK and with version 1 you can use VMware or KVM as a chose your own hypervisor. Version 2 will come out later this year and will be based on a complete VMware stack known as EVO: RACK.

Storage services are supplied by EMC ScaleIO. You can purchase a 1/4 rack, 1/2  rack or full rack which includes top of rack networking. You can also scale out by adding more full racks to the system. EMC said that it can technically support 1000s of racks VSPEX Blue appliances for up to ~38PB of storage.

The significant thing is that the VCE VxRACK supplies the VCE customer experience, in a hyper converged solution. However, the focus for VxRACK is tier 2 applications that don’t have a need for the extremely high availability, low response times and high performance of tier 1 applications that run on their VBLOCK solutions (with VNX, VMAX or XtremIO storage).


They had a 5th grader provision an VMAX3 gold storage (LUN) and convert it to a diamond storage (LUN) in 20.48 seconds. It seemed pretty simple to me but the kid blazed through the screens a bit fast for me to see what was going on. It wasn’t nearly as complex as it used to be.

VMAX3 also introduces CloudArray™, which uses FastX storage tiering to cloud storage (using onboard TwinStrata software). This could be used as a tier 3 or 4 storage. EMC also mentioned that you can have an XtremIO (maybe an Xbrick) behind a VMAX3 storage system. VMAX3’s software rewrite has separated data services from backend storage and one can see EMC rolling out different backend storage (like cloud storage or XtremIO) in future offerings.

Other Notes

There was a lot of discussion about the “Information Generation” a new customer for IT services. This is tied to the 3rd platform transformation that’s happening in the industry today. To address this new world IT needs to have 5 attributes:

  1. Predictively spot new opportunities for services/products
  2. Deliver a personalized experience
  3. Innovate in an agile way
  4. Develop trusted programs/apps Demonstrate transparency & trust
  5. Operate in real time

David Goulden talked a lot about what this all means and I encourage you to take a look at the video stream to learn more.

Speaking of video last year was the first year there were more online viewers of EMCWorld than actual participants. So this year EMC upped their game with more entertainment value. The opening dance sequence was pretty impressive.

A lot of talk today was on 3rd platform and the transition from 2nd platform. EMC says their new products are Platform 2.5 which are enablers for 3rd platform. I asked the question what the 3rd platform storage environment looks like and they said scale-out (read ScaleIO) converged storage environment with flash for meta-data/indexing.

As the 3rd platform transforms IT there will be some customers that will want to own the infrastructure, some that will want to use service providers and some that will use public cloud services. EMC’s hope is to capture those customers that want to own it or use service providers.

Tomorrow the focus will be on the Federation with Pivotal and VMware being up for keynotes and other sessions. Stay tuned.