Using jell-o (hydrogel) for new form of photonics computing

Read an article the other day which blew me away, Researchers Create ” Intelligent interaction between light and meterial – New form of computing, which discussed the use of a hydrogel (like raspberry jell-o) that could be used both as a photonics switch for optical communications and as modifiable material to create photonics circuits. The research paper on the topic is also available on PNAS, Opto-chemical-mechanical transduction in photeresponsive gel elicits switchable self trapped beams with remote interactions.

Apparently researchers have created this gel (see B in the graphic above)which when exposed to laser light interacts to a) trap the beam within a narrow cylinder and or b) when exposed to parallel beams interact such that it boosts the intensity of one of the beams. They still have some work to show more interactions on laser beam(s) but the trapping of the laser beams is well documented in the PNAS paper.

Jell-o optical fibres

Most laser beams broaden as they travel through space, but when a laser beam ise sent through the new gel it becomes trapped in a narrow volume almost as if sent through a pipe.

The beam trading experiment using a hydrogel cube of ~4mm per side. They sent a focused laser beam with a ~20um diameter through an 4mm empty volume and measured the beam’s disbursement to be ~130um diameter. Then the did the same experiment only this time shining the laser beam through the hydrogel cube and over time (>50 seconds) the beam diameter narrows to becomes ~22um. In effect, the gel over time constructs (drills) a self-made optical fibre or cylindrical microscopic waveguide for the laser beam.

A similar process works with multiple laser beam going through the gel. More below on what happens with 2 parallel laser beams.

The PNAS article has a couple of movies showing the effect from the side of the hydrogel. with a single and multiple laser beams.

Apparently as the beam propagates through the hydrogel, it alters the optical-mechanical properties of the material such that the refractive index within the beam diameter is better than outside the beam diameter. Over time, as this material change takes place, the beam diameter narrows back down to almost the size of the incoming beam. They call any material like this that changes its refractive index as chromophores.

It appears that the self-trapping effectiveness is a function of the beam intensity. That is higher intensity incoming laser beams (6.0W in C above) cause the exit beam to narrow while lower (0.37W) intensity incoming laser beams don’t narrow as much.

This self-created optical wave-guide (fibre) through the gel can be reset or reversed (> 45 times) by turning off the laser and leaving the gel in darkness for a time (200 seconds or so). This allows the material to be re-used multiple times to create other optical channels or to create the same one over and over again.

Jell-o optical circuits

It turns out that by illuminating two laser beams in parallel their distances apart can change their interaction even though they don’t cross.

When the two beams are around 200um apart, the two beams self channel to about the size of ~40um (incoming beams at ~20um). But the intensity of the two beams are not the same at the exit as they were at the entrance to the gel. One beam intensity is boosted by a factor of 12 or so and the other is boosted by a factor of 9 providing an asymmetric intensity boost. Unclear how the higher intensity beam is selected but if I read the charts right the more intensely boosted beam is turned on after the the less intensely boosted beam (so 2nd one in gets the higher boost.

When one of the beams is disabled (turned off/blocked), the intensity of the remaining beam is boosted on the order of 20X. This boosting effect can be reversed by illuminating (turning back on/unblocking) the blocked laser. But, oddly the asymmetric boosting, is no longer present after this point. The process seemingly can revert back to the 20X intensity boost, just by disabling the other laser beam again. .

When the two beam are within 25 um of each other, the two beams emerge with the same (or close to similar) intensity (symmetric boosting), and as you block one beam the other increases in intensity but not as much as the farther apart beams (only 9X).

How to use this effect to create an optical circuit is beyond me but they haven’t documented any experiments where the beams collide or are close together but at 90-180 degrees from one another. And what happens when a 3rd beam is introduced? So there’s much room for more discovery.

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Just in case you want to try this at home. Here is the description of how to make the gel from the PNAS article: “The polymerizable hydrogel matrix was prepared by dissolving acrylamide:acrylic acid or acrylamide:2-hydroxyethyl methacrylate (HEMA) in a mixture of dimethyl sulfoxide (DMSO):deionized water before addition of the cross-linker. Acrylated SP (for tethered samples) or hydroxyl-substituted SP was then added to the unpolymerized hydrogel matrix followed by an addition of a catalyst. Hydrogel samples were cured in a circular plastic mold (d = 10 mm, h = 4 mm thick).

How long it will take to get the gel from the lab to your computer is anyones guess. It seems to me they have quite a ways to go to be able to simulate “nor” or “nand” universal logic gates widely used in to create electronic circuits today.

On the other hand, using the gel in optical communications may come earlier. Having a self trapping optical channel seems useful for a number of applications. And the intensity boosting effect would seem to provide an all optical amplifier.

I see two problems:

  1. The time it takes to get to a self trapping channel, 50sec is long and it will probably take longer as you increase the size of the material.
  2. The size of the material seems large for optical (or electronic) circuitry. 4mm may not be much but it’s astronomical compared to the nm used in electronic circuitry

The size may not be a real concern as the movies don’t seem to show that the beam once trapped changes across the material, so maybe it could be a 1mm, or 1um cube of material that’s used instead. The time is a more significant problem. But then again there may be another gel recipe that acts quicker. But from 50sec down to something like 50nsec is nine orders of magnitude. So there’s a lot of work here.

Comments?

Photo Credit(s): all charts are from the PNAS article, Opto-chemo-mechanical transduction in photo responsive gel…

Gaming is driving storage innovation at WDC

I was at SFD19 a couple of weeks ago and Western Digital supplied the afternoon sessions on their technology (see videos here). Phil Bullinger gave a great session on HDDs and the data center market. Carl Che did a session on HDD technology and discussed on how 5G was going to ramp up demand for video streaming and IoT data requirements. Of course one of the sessions was on their SSD and NAND technologies.

But the one session that was pretty new and interesting to me was their discussion on how Gaming and how it’s driving system innovation. Eric Spaneut, VP of Client Computing was the main speaker for the session but they also had Leah Schoeb, Sr. Developer Manager at AMD, to discuss the gaming market and its impact on systems technology.

There were over 100M viewers of the League of Legends World Championships, with a peak viewership of 44M viewers. To put that in perspective the 2020 Super Bowl had 102M viewers. So gaming championships today are almost as big as the Super Bowl in viewership.

Gaming demands higher performing systems

Gaming users are driving higher compute processors/core counts, better graphics cards, faster networking and better storage. Gamers are building/buying high end desktop systems that cost $30K or more, dwarfing the cost of most data center server hardware.

Their gaming rigs are typically liquid cooled, have LEDs all over and are encased in glass. I could never understand why my crypto mine graphics cards had LEDs all over them. The reason was they were intended for gaming systems not crypto mines.

Besides all the other components in these rigs, they are also buying special purpose storage. Yes storage capacity requirements are growing for games but performance and thermal/cooling have also become major considerations.

Western Digital has dedicated a storage line to gaming called WD Black. It includes both HDDs and SSDs (internal NVMe and external USB/SATA attached) at the moment. But Leah mentioned that gaming systems are quickly moving away from HDDs onto SSDs.

Thermal characteristics matter

Of the WDC’s internal NVMe SSDs (WD Black SN750s), one comes with a heat sink attached. It turns out SSD IO performance can be throttled back due to heat. The heatsink allows the SSD to operate at higher temperatures and offer more bandwidth than the one without. Presumably, it allows the electronics to stay cooler and thus stay running at peak performance.

I believe their WD Black HDDs have internal fans in them to keep them cool. And of course they all come in black with LEDs surrounding them.

Storage can play an important part in the “gaming experience” for users once you get beyond network bottlenecks for downloading. For downloading and storage perform well . however for game loading and playing/editing videos/other gaming tasks, NVMe SSDs offer a significant performance boost over SATA SDDs and HDDs.

But not all gaming is done on high-end gaming desktop systems. Today a lot of gaming is done on dedicated consoles or in the cloud. Cloud based gaming is mostly just live streaming of video to a client device, whether it be a phone, tablet, console, etc. Live game streaming is almost exactly like video on demand but with more realtime input/output and more compute cores/graphic engines to perform the gaming activity and to generate the screens in “real” time. So having capacity and performance to support multiple streams AND the performance needed to create the live, real time experience takes a lot of server compute & graphics hardware, networking AND storage.

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So wherever gamers go, storage is becoming more critical in their environment. Both WDC and AMD see this market as strategic and growing, whose requirements are unique enough to demand special purpose products. They bothy are responding with dedicated hardware and product lines tailored to gaming needs.

Photo credit(s): All graphics in this post are from WDC’s gaming session video stream

Breaking IoT security

Earth globe within a locked cage

Read an article the other day (Researchers exploit low entropy of IoT devices to break RSA certificates) about researchers cracking IoT device security and breaking their public key encryption keys. The report focused on PKI and RSA certificates and IoT devices. The article mentioned the research paper describing the attack in more detail.

safe 'n green by Robert S. Donovan (cc) (from flickr)
safe ‘n green by Robert S. Donovan (cc) (from flickr)

RSA certificates publish a public key and the digital signature of the certificate and identify the device that owns the certificate.

What the researchers were able to show was that ~250K keys in IoT device RSA certificates were insecure. They were able to compromise the 250K RSA certificates using a single Microsoft Azure VM and about $3K of computer time.

It turns out that if two RSA certificate public keys share the same factor, it’s much easier to determine the greatest common devisor GCD) of the two public keys than it is to factor any one of them. And once you have the GCD of the two keys, it’s relatively trivial to determine the other factor in a public key. And that’s just what they did.

Public key infrastructure (PKI) encryption depends on asymmetric cryptography using a “public” key to encrypt messages (or to encrypt a one time key to be used in later encryption of messages) and the use of a “private” key to decrypt the message (or keys) and sign digital certificates. There are certificate authorities and a number of other elements used in PKI but the asymmetric cryptography at its heart, rests on the foundation of the difficulty in factoring large numbers but those large numbers need to be random and prime.

True randomness is hard

Just some of the recently donated seeds that are being added to the Reading Food Growing Network seed swap boxes, including some Polish gherkin seeds.

The problem starts with generating truly random numbers in a digital computer. Digital algorithms typically depend on a computer to perform the some set of instructions, in the same way and sequence so as to get the same answer every time we run the algorithm.

But if you want random numbers this predictability of always coming up with the same answer each time results in non-random numbers (or rather random numbers that are the same each time you run the algorithm). So to get around this, most random number generators can make use of a (random) seed which is used as an input to the algorithm to generate random numbers.

However, this seed needs to be a random number. But to create a random number it needs to be generated not with instructions but using something outside the digital computer. One approach noted above is to use a human typing keys to generate a random number to be used as a seed.

The researchers exploited the fact that most IoT devices don’t use a random (enough) seed for their PKI key generation. And they were able to use the GCD trick to figure out the factors to the PKI.

But the lack of true randomness (or entropy) is the real problem. Somehow, these devices need to have a cheap and effective way to generate a random seed. Until this can be found, they will be subject to these sorts of attacks.

… but not impossible to obtain

I remember in times past when tasked to create a public key-private key pair I had to type some random characters. The Public key encryption algorithm used the inter-character time interval of my typing to generate a random seed that was then used to generate the key pair used in the public key. I believe the two keys also need to be prime numbers.

Earth globe within a locked cage

Perhaps a better approach would be to assign them keys from a centralized key distributor. That way the randomness could be controlled by the (key) distributor.

There are other approaches that depend on the sensors available to an IoT device. If the device has a camera or mic, taking raw data from the camera or sound sensor and doing a numerical transform on them may suffice. Strain gauges, liquid levels, temperature, humidity, wind speed, etc. all of these devices have something which senses the world around them and many of these are, at their base, analog sensors. Reading and converting some portion of these analog signals from raw analog to a digital random seed could be very effective way to generate true(r) randomness.

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The paper has much more information about the attack and their results if your interested. They said that ~50% of the compromised devices were from a large network supplier. Such suppliers probably also have a vast majority of devices deployed. Still it’s troubling, nonetheless.

Until changes are made to IoT devices, they will continue to be insecure. Not as much of a problem when they are read only sensors but when the information they sense is used by robots or other automation to make decisions about actions, then having insecure IoT becomes a safety issue.

This is not the first time such an attack was attempted and each time, it’s been very successful. That alone should be cause for alarm. But IoT and similar devices are hard to patch in the field and their continuing insecurity may be more of a result of the difficulty of updating a large install base than anything else.

Photo Credit(s):

Designing living machines

Read an article the other day in PNAS (A scaleable pipeline for designing reconfigurable organisms) which described an approach to designing and constructing living organisms to perform real world actions. One could call these living machines or biological robots (biobots). There’s an appendix to the paper which provides supplementary information.

The intention of the pipeline is to expand the modern design space from construction materials, chemical process, electronics and mechanical devices to the domain of living things. Thereby create objects that perform functions for mankind, that are operate well with living things, are more resilient and have a benign impact on the environment.

The Biobot design pipeline stage 1

The design process begins using an evolutionary algorithm which takes as input an organism goal or action (i.e., moving so many body lengths for minutes) and the cell types to be used in constructing the organism and randomly generates potential organism designs.

In the current process there are two cell types (red and cyan) one is passive (scaffolding) and the other is active and provides movement power.

Designing and manufacturing reconfigurable organisms. A behavioral goal (e.g., maximize displacement), along with structural building blocks [here, contractile (red) and passive (cyan) voxels], are supplied to an evolutionary algorithm. The algorithm evolves an initially random population and returns the best design that was found.

Once a set of randomized designs using the two cell types have been determined, each undergoes a computerized simulation (in a physics engine that simulates gravity and liquid environment) to see how well the possible organism perform.

All designs are ranked in how well the achieve they performe and the best of these are used as seeds for another round of evolutionary design exploration. This uses these good designs and randomly changes some aspect of them to create another set of organism designs to test out.


Designing reconfigurable organisms. For a given goal, 100 independent evolutionary trials were conducted in silico (A–C). Each colored line represents the velocity of the fastest-moving design within its clade. Each genome (D) dictates anatomy and behavior by determining where and how voxels are combined, and whether they are passive (cyan) or contractile (red; E).

At some point, the evolutionary design exploration-simulation process stops when it has determined a set of workable organism designs which can achieve the goals set out for them.

The workable organism designs are then subjected to two rounds of filtering. The first filter is tests the designs for resilience to noise. This is done by putting the designs through another set of computer simulations that include noise. Some of the workable organism designs will still perform well in noisy environment and others will not.

The organism designs that perform well in noisy environments, are deemed resilient and are then fed into the next filtering stage of the pipeline.

The resilient workable organism designs are then filtered by whether they can be constructed with the current processes. Even though all the organisms are made up of the two cell types, not all of them can be realized given the current process.

After this point we have a set of designs that

a) Achieve the requested goal in simulation;

b) Perform well in noisy environment simulations; and

c) Can be constructed with the current processes and cell types.

The Biobot design pipeline stage 2

The next steps in the organism design pipeline all take place in the real world. The set of selected designs are constructed/manufactured and set into a petri dish to see how well they perform in real life .

The two cell types used in the current process are derived from the Xenobus (frog) embryos and consist of stem cells (passive) and heart muscle cells (active). The building of organism designs is done through layering of stem cells and then surgically or using cauterization to remove cells not part of the design. After the stem cells are placed then heart muscle cells can be layered on in a similar fashion.

There’s no control mechanism whatsoever other than the surfaces designed for the organism. Xenobus heart muscle cells automatically contract and when combined with other heart muscle cells, all the muscle cells contract in waves.

The design of the organism is such that the contractions propel the organism to move and explore the environment (the intended goal). The designed organisms are placed in a Petri dish and then observed over a period of time to see how well the perform the desired action.

Successful designs can then be seeded back into the start of the evolutionary exploration to generate even better designs. Simulations can also be adjusted with feedback from the real world behavior of the designed organisms. At some point the best designed organisms can be used in the real world.

Why biobots

Although the example had a goal to explore its environment. other goals could be readily used as well. Some of the ones mentioned in the paper are manipulating and gathering together some compounds/elements/particles in a volume. These could be used to clear a viscous solution of some impurities.

Another organism could be designed to have a pouch within which they can store and transport objects (or drugs).

Designed organisms could operate together in a solution with some organisms performing one function while others perform other functions. Organism designs could eventually be combined into one organism that performs more functions.

One nice aspect of biobots is that they can be squeezed, perturbed in many ways including being cut and they repair themselves and continue to operate.

One could design an organism to reproduce in a suitable environment or even designed to age and die after a specific time period.

The end goal seems to be to create living machines that can be used to operate in the environment or a body. Biobots could be designed to clear away plaque from a blood vessels or to dismantle malignant tumors. They could conceivably be constructed from a person’s own cells to operate for days-weeks-months in a body and then dissolve to be reused/disposed of just like any other biological material in a human.

So now we can design biobots.

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Just in case you wanted to try your hand at designing living organisms yourself. The researchers have all open sourced thei code for the evolutionary design exploration, computerized simulation, noise and build ability filtering which is available on github. The actual manufacturing/construction of the designed organism would need to be done in a lab.

Photo credit(s): All images are from the paper and its supplementary appendix.

Weight Agnostic Neural Networks (WANNs)

Read an article the other day (Neural Networks Can Drive Without [weight] Learning) about a new form of deep learning neural network (NN) that is not dependent on the weights assigned to network nodes. The new NN is called WANN (Weight Agnostic NN). There’s also a scientific paper (on Github, Weight Agnostic Neural Networks) that describes WANNs in more detail.

How WANNs differ from normal NN

If I understand them properly, WANNs are trained, but instead of assigning weights during training, WANN networks architectures (nodes and connections) are modified and optimized to perform well against the training data.

Indeed, most NN start out with assigning random weights to all network nodes and then these weights are adjusted through the training cycle, until the NN performs well on the training data. But NN such as these, have a structure (# nodes/layer, # layers, connectivity type, etc.) defined by the researcher, that is stable and unchanging during a training-validation cycle. If the NN model is not accurate enough, the researcher has two choices, find better data or change the model’s structure. WANNs start and end with changing the model’s structure.

With WANNs they start out with a set of NN architectures (#nodes/layer, #layers, connection types, etc). Each NN architecture is evaluated against the training data with a single shared randomized weight. That shared weight is altered (randomly) for a training pass and the model evaluated for accuracy.

At the end of a WANN training pass you have a set of evaluation metrics for each model structure. The resultant WANNs are then ordered by performance and complexity. The highest performing networks are then used to create a new population (set) of WANN architecture to be tested and the process iterates from there. This would presumably continue until you have reached a plateau of accuracy statistics across a number of shared randomized weights. And this would be the WANN model used for the application

Why WANN?

For a normal NN, each node weight would be adjusted automatically and independently at the end of each training batch. There would, of course, be a large number of batches, causing each weight in the NN nodes to be altered (via floating point arithmetic). So the math would be floating point arithmetic*#nodes*#layers*# of training batches (* # training passes (or epochs).

WANNs avoid this inner loop math altogether. Instead they would need to test a model on a number of shared random weights. This would presumably be done after a complete training pass (each epoch). And even if you had the same number of WANN models as nodes in a normal NN, the computations would be much less. Something on the order of #models * # epochs (each training pass [or epoch] could conceivable test a different shared random weight).

Another advantage of WANNs is that they result in simpler, less complex NN models (# nodes, # layers, # of connections, etc.) than normal DL NNs. Simpler NN models could be very useful for IoT applications, where computational power and storage is limited.

The main disadvantage of WANNs is that they aren’t as accurate as normally (weight adjusted) NNs. However, once you have a WANN, you can always elect to re-train it in the normal fashion by adjusting weights to gain more accuracy. And doing so would likely be much closer to a more complex NN model that was trained from the start by altering weights.

WANNs are more like nature

Human and other mammal (probably avian, aquatic, etc as well) seem to be born with certain innate abilities, visual, perceptive, mobility and with certain habits such as nursing, facial mimicking, hunger-feeding, etc. Presumably these innate abilities and habits are hardwired neuron networks that don’t depend on envirnonmental learning. Something that they are all born with.

Concievably WANNs could be consider similar to these hardwired (unlearned) neuron networks. WANNs could be used in a similar fashion to embed certain innate habits and abilities into robots or other automation that could be further trained with their interactions with their environment

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The Github paper has an online WANN model widget with a slider where you can alter a shared random weight and see its impact on the operation of a the widget. Playing with this, the only weight that seems to have a significant impact on the actions of the widget is zero…

Photo Credit(s): “Neural Connections In the Human Brain” by Image Editor is licensed under CC BY-NC-ND 2.0 

Internet of Tires

Read an article a couple of weeks back (An internet of tires?… IEEE Spectrum) and can’t seem to get it out of my head. Pirelli, a European tire manufacturer was demonstrating a smart tire or as they call it, their new Cyber Tyre.

The Cyber Tyre includes accelerometer(s) in its rubber, that can be used to sense the pavement/road surface conditions. Cyber Tyre can communicate surface conditions to the car and using the car’s 5G, to other cars (of same make) to tell them of problems with surface adhesion (hydroplaning, ice, other traction issues).

Presumably the accelerometers in the Cyber Tyre measure acceleration changes of individual tires as they rotate. Any rapid acceleration change, could potentially be used to determine whether the car has lost traction due and why.

They tested the new tires out at a (1/3rd mile) test track on top of a Fiat factory, using Audi A8 automobiles and 5G. Unclear why this had to wait for 5G but it’s possible that using 5G, the Cyber Tyre and the car could possibly log and transmit such information back to the manufacturer of the car or tire.

Accelerometers have become dirt cheap over the last decade as smart phones have taken off. So, it was only a matter of time before they found use in new and interesting applications and the Cyber Tyre is just the latest.

Internet of Vehicles

Presumably the car, with Cyber Tyres on it, communicates road hazard information to other cars using 5G and vehicle to vehicle (V2V) communication protocols or perhaps to municipal or state authorities. This way highway signage could display hazardous conditions ahead.

Audi has a website devoted to Car to X communications which has embedded certain Audi vehicles (A4, A5 & Q7), with cellular communications, cameras and other sensors used to identify (recognize) signage, hazards, and other information and communicate this data to other Audi vehicles. This way owning an Audi, would plug you into this information flow.

Pirelli’s Cyber Car Concept

Prior to the Cyber Tyre, Pirelli introduced a Cyber Car concept that is supposedly rolling out this year. This version has tyres with real time pressure, temperature, (static) vertical load and a Tyre ID. Pirelli has been working with car manufacturers to roll out Cyber Car functionality.

The Tyre ID seems to be a file that can include anything that the tyre or automobile manufacturer wants. It sort of reminds me of a blockchain data blocks that could be used to validate tyre manufacturing provenance.

The vertical load sensor seems more important to car and tire manufacturers than consumers. But for electrical car owners, knowing car weight could help determine current battery load and thereby more precisely know how much charge is left in a battery.

Pirelli uses a proprietary algorithm to determine tread wear. This makes use of the other tyre sensors to predict wear and perhaps uses an AI DL algorithm to do this.

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ABS has been around for decades now and tire pressure sensors for over 10 years or so. My latest car has enough sensors to pretty much drive itself on the highway but not quite park itself as of yet. So it was only a matter of time before something like smart tires would show up.

But given their integration with car electronics systems, it would seem that this would only make sense for new cars that included a full set of Cyber Tyres. That is until all tire AND car manufacturers agreed to come up with a standard protocol to communicate such information. When that happens, consumers could chose any tire manufacturer and obtain have similar if not the same functionality from them.

I suppose someone had to be first to identify just what could be done with the electronics available today. Pirelli just happens to be it for now in the tire industry.

I just don’t want to have to upgrade tires every 24 months. And, if I have to wait a long time for my car to boot up and establish communications with my tires, I may just take a (dumb) bike.

Photo Credit(s):

Made in space

Read an article in IEEE Spectrum recently titled, 4 Products it makes sense to manufacture in space. The 4 products identified in the article include:

1) Metal alloys – because of micro-gravity, the mixture of metals that go into metal alloys should be much more even and as a result, should create a purer mixture of the metal alloy at the end of the process.

2) Fibre optical cables – the article says, ZBLAN, which is a heavy-metal fluoride glass fibre could have 1/10th the signal loss of current cable but is hard to manufacture on earth due to micro-crystal formation. Apparently, when manufactured (mixed-drawn) in micro-gravity, there’s less of this defect in the glass.

3) Printed human organs – the problem with printing biological organs, hearts, lungs, livers, etc. is they require scaffolding for the cells to adhere to that needs to be bio-degradeable and in the form of whatever organ is needed. However, in micro-gravity there should be less of a need for any scaffolding.

4) Artificial meat – similar to human organs above, by being able to build (3D print) biological products, one could create a steak or other cuts of meat that biological #D printing.

Problems with space manufacture

One problem with manufacturing metal alloys and fibre optic cable in space, is the immense heat required. Glass melts at 1400C, metals anywhere from 650C to 3400C. Getting rid of all that heat in space could present a significant problem. Not to mention the vessels required to hold molten materials weigh a lot.

And metal and glass manufacturing processes can also create waste, such as hot metal/glass particulates that settle on the floor on earth, but who knows where in space. To manufacture metal or glass on ISS would require a very heat tolerant, protected environment or capsule, lots of power to provide heat and radiator surfaces to release said heat.

And of course, delivering raw materials for metals and glass to space (LEO) would cost a lot (SpaceX $2.7K/kg , Atlas V $13.2K/kg). As such, the business case for metal alloy manufacturing in space doesn’t appear positive.

But given the reduced product weight and potentially higher prices one can charge for the product, fibre pptical glass may make business sense. Especially, if you could get by with 1/10th the glass because it has 1/10th the signal loss.

And if you don’t have to ship raw materials from earth (using the moon or asteroids instead), it would improvesboth business cases. That is, assuming raw material discovery and shipping costs are 1/6th or less as much as shipping from earth.

As for organs, as they can’t be manufactured on earth (yet), it could be the “killer app’ for made in space. But it’s sort of a race against time. Doing this in space may be a lot easier today but more research is going on to create organs on earth than in space. But eventually, manufacturing these on earth could be a lot cheaper and just as effective.

But I don’t see a business case for meat in space unless it’s to support making food for astronauts on ISS. Even then, it might be cheaper to just ship them some steak.

Products hard to make in space

I would think anything that doesn’t require gravity to work, should be easier to produce in space.

But that eliminates distillation, e.g., fossil fuel refining, fermentation, and many other chemical distillation processes (see Wikipedia article on Distillation).

But gravity is also used in depositing and holding multiple layers onto one another. So manufacturing paper, magnetic/optical disk platters, magnetic tapes, or any other product built up layer by layer, may not be suitable for space manufacture.

Not sure about semiconductors, as deposition steps can make use of chemical vapors. And that seems to require gravity. But it’s conceivable that in the absence of gravity, chemicals may still adhere to the wafer surface, as it’s an easier location to combine with than other surfaces in the chamber. On the other hand, they may just as likely retain their mixture in the vapor.

Growing extremely pure silicon ingots may be something better done in space. However, it may suffer from the same problems as metal alloy manufacturing. Given the need for extreme purity and the price paid for pure silicon, I would think this would be something to research ahead of metal alloys.

For further research

But in the end, if and when we become a space fairing people, we will need to manufacture everything in space. As well as grow or find raw materials easier than shipping them from the earth.

So, some research ought to be directed on how to perform distillation and multi-layer product manufacturing in space/micro-gravity. Such processes could potentially be done in a centrifuge, if they truly can’t be gone without gravity.

It’s also unclear how to boil any liquid in 0g or micro-g without convection (see Bizarre Boiling NASA Science article). According to the article, it creates one big bubble that stays where it is formed. Providing some way to extract this bubble in place would seem difficult. Boiling liquids in a centrifuge may work.

In any case, I’m sure the ISS crew would be more than happy to do any research necessary to figure out how to brew beer, let alone, distill vodka in space.

Picture Credit(s):

Supercomputing 2019 (SC19) conference

I was at SC19 last week and as always there was lots to see on the expo floor and at the show in general. Two expo booths that I thought were especially interesting were:

  • Zapata Computing systems – a quantum computing programming for hire outfit and
  • Cerebras – a new AI wafer scale accelerator chip that sported 400K+ cores in a single package.

Zapata Computing, quantum coding for hire

We’ve been on a sort of quantum thread this past month or so (e.g., see our Quantum computing – part 2 and part 1, The race for quantum supremacy posts). Zapata Computing was at the edge of the exhibit floor in a small booth pretty much just one guy (Michael Warren) and their booth with some handouts. Must have had something on the booth about quantum computing, because I stopped by

Warren said they have ~20 PhDs, from around the world working for them and provide quantum coding for hire. Zapata works with organizations to either get them up to speed on quantum programing or write quantum programs themselves under contract for clients and help run them on quantum computers.

Zapata’s quantum algorithms are designed to run on any type of quantum computer such as ion trap, superconducting qubit, quantum annealers, etc. They also work with Microsoft Azure Quantum, IBM Q, Rigetti, and Honeywell systems to run quantum programs for customers. Notably missing from this list was Google and Honeywell is new to me but seem active in quantum computing.

Zapata has their own Orquestra quantum toolkit. We have discussed quantum software development kits like IBM Q Qiskit previously but Microsoft has their own, QDK and Rigetti has Forrest SDK. So, presumably, Orquestra front ends these other development kits. Couldn’t find anything on Honeywell but it’s likely they have their own development kit as well or make use of others.

In talking to the Warren at the show, Zapata is working to come up with a quantum computing cloud, which can be used to run quantum code on any of these quantum computers with the click of a button. Warren sounded like this was coming out soon.

Some of the Zapata Computing quantum programs they have developed for clients include: logistic simulations, materials design, chemistry simulations, etc.

Warren didn’t mention the cost of running on quantum computers but he said that some companies are more forthright with pricing than others. It seemed Rigetti had a published price list to use their systems but others seemed to want to negotiate price on a per use basis.

It seems only a matter of time before quantum computing becomes just like GPUs. Just another computational accelerator that works well for some workloads but not others. Zapata Computing and Orquestra are just steps along this path.

Cerebras

AI accelerator chips have also been a hot topic for us (see our posts on Google TPU, GraphCore’s system, and the Mythic’s and Syntiant’s AI accelerators). But none,. with the possible exception of GraphCore, has taken this on to quite the same level as Cerebras.

Cerebras offers a wafer scale chip that is embedded into their CS-1 system. The chip has 400K cores, 18GB of (very fast) SRAM (memory), 100Pb/sec (peta-bits or 10**15 bits per second) of bandwidth and draws ~20kW. Their CS-1 system fits in a standard rack taking up 15U of space.

The on-chip fabric is called SWARM which supports a 2D mesh. The SWARM mesh is entirely configurable, to support optimal neural network connectivity. I assume this means that any core can talk directly (with 0 hops) to any other core on the chip through a configuration setup.

The high speed on chip SRAM supports up to 9PB/sec of memory bandwidth and can be accessed in a single clock cycle. They call the cores Sparse Linear Algebra Compute (SLAC) cores and say that they are optimized to support ML-DL computations, which we assume meansfloating point aritmetic.

Although you can’t really see the (wafer scale) chip in the picture above, it’s located in the section between the copper plate and the copper heat sink and is starts at the copper line between the two. CS-1 consumes a lot of power and much of its design is to provide proper cooling. One can view some of that on the left side of the picture above.

As for software, Cerebras CS-1 supports TensorFlow and PyTorch as well as standard C++. Their Cerebras Software Platform stack, consists of two layers: the Cerebras Intermediate Representation and Cerebras Graph Compiler (CGC) that feeds their Cerebras Wafer Scale Engine (WSE). The CGC maps neural network nodes to cores on the WSE and probably configures SWARM to provide NN core to NN core connectivity.

It’s great to see hardware innovation again. There was a time where everyone thought that software alone was going to kill off hardware innovation. But the facts are that both need to innovate to take computing forward. Cerebras didn’t tell me any PetaFlop rate for their system and but my guess it would beat out the 2PFlop GraphCore2 (GC2) system but it’s only a matter of time before GC3 comes out. That being said, what could be beyond wafer scale integration?

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I enjoy going to SC19 for all the leading edge technology on display. They have some very interesting cooling solutions that I don’t ever see anywhere else. And the student competition is fun. Teams of students running HPC workloads around the clock, on donated equipment, from Monday evening until Wednesday evening. With (by SC19) spurious fault injection to see how they and their systems react to the faults to continue to perform the work needed.

For every SC conference, they create an SCinet to support the show. This year it supported Tb/sec of bandwidth and the WiFi for the floor and conference. All the equipment and time that goes into creating SCinet is donated.

Unfortunately, I didn’t get a chance to go to keynotes or plenary sessions. I did attend one workshop on container use in HPC and it was completely beyond me. Next years, SC20 will be in Atlanta.

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