BrainChip Holdings Ltd (BRN) Earnings Call Transcript & Summary

Good afternoon, everyone. This is Lou DiNardo, and I hope everyone can hear me. If anybody has a problem, you can send me a note, or send Roger Levinson a note. Thank you for joining us on the 2019 year-end update. We put a press release out yesterday. The update was lodged with the ASX yesterday. And I hope you all had a chance to review it. I think what you'll notice here is I'm using exactly that as the foundation for today's webinar, but we'll provide a bit more color commentary as well as address a bunch of questions that were sent in, which I thank you for. Now before I start, I wanted to reach out and give a special thank you to Peter Van der Made. Peter has dedicated a great deal of his professional career in developing Akida, our neuromorphic solution. I'm sure he takes great pride in the work that's been accomplished and the team's effort. I'd also like to thank Anil Mankar, our Chief Development Officer, who has worked tirelessly, I mean literally 8 days a week. He and his team have done an exceptional job of reducing to practice and getting the logic designed on, and are now ready to hand off. And maybe one more somewhat somber note, just reaching out to all of our friends there in Sydney. I hope you're coping as best as possible with those rampant fires. Now ironically, San Francisco also [indiscernible] or Bay City and California more generally, we've suffered very similar challenges over the last several months. So I hope everyone is well and that soon passes. I'm going to jump into the presentation now. I'll try and keep you guys on what page I'm on here if you're doing this with a hard copy. For some reason, this is -- there we go. So as we end our financial year of 2019, a couple of the highlights that I'm going to touch on here. I'll also touch on some of the licensing, we can't ignore those. The introduction of the acute intellectual property for licensing to ASIC suppliers was a very big achievement, one that's getting great momentum and steam behind it. The introduction of the neural network converter for CNN. And this is important to note, CNN to event-based CNN, just kind of a middle ground as well as Native SNN translation. A great deal of activity going on with the conversion to event-based CNNs and similar activity going on in the development of native SNN networks. Of course, I think you're all aware, the definitive agreement that we signed with Socionext for the Akida development and manufacturing, that process has gone very well. I'll touch on that relationship and where we are in that cycle in just a few minutes. A couple of very important patents that were filed for Akida inventions, those were provisional patents, and I'll touch on this a bit in a bit as well. But there's provisional patents, which give us our priority date, which is first and foremost important. And then we have 1 year from that provisional filing to reduce those to practice as utility patents, and each will spawn a number of utility patents. Answering one of the questions that I got, having the patents filed provisionally does not impede in any way our [Audio Gap] the device when it comes out. Having those priority dates set was basically the most important thing. I know Peter and our legal team is working on the utility patents. As you know, we raised about $2.85 million in a convertible note that was issued and an entitlement offering that in Aussie dollars was AUD 10.7 million. I'll touch a bit on the expansion of our sales and marketing team. We've brought in a group that is really well skilled in intellectual property licensing and sales to ASIC suppliers as well as design services companies. Again, addressing a question that was asked, the size of the market we're really going to do with Akida, whether it's IP going into someone else's system-on-chip or whether it's a device, whether it's a card-level product or [Audio Gap] see here. This is the entire chipset market for AI. And you can see that between now and 2025, that market's expected to grow from roughly $5 billion to a bit over $70 billion. What's more important is when you look at the edge, which is colored orange, the bottom [Audio Gap] of these bars. That market is going to grow from something slightly less than $5 billion to something over $50 billion, close to $55 billion. When we talk about edge, this is where we are targeting Akida. It is an ultralow power, high-performance, complete network on the chip. So when you think about putting things into a module for ADAS, whether it's ultrasound LiDAR radar, when you think about smart cameras, where you want to do analytics at the point of the camera rather than sending all of the data back across a bus or across a wireless network, we can do the analytic at the camera, determine if it's a person, if it's a face, if it's a known face or if it's an unknown face. Similarly, in ADAS, we can determine -- based on the LiDAR data, for example, which we're working hard on, we can determine what's in front of the car and not to send all of the data back to a big GPU that's sucking up lots of power. So I think it's important to note, this is a large market. It's not going to be a winner-take-all market. There'll be lots of different types of solutions. But we'll talk about Akida and its features and benefits, which has been exceptionally well received. So lots of questions about the development and where we are. So just putting it in its most simple terms. [Audio Gap] layout are essentially complete, tying down loose ends, getting things ready to hit the button and go to what we call tape-out when we generate the file, which will then generate the wafer masks. So the Akida logic design has been well wrung out, both in simulation on the Akida development environment as well as internally on an FPGA that emulates the actual hardware design. And that is an important part of the process. You do simulation. You do emulation. All has gone quite well. Next big part in the process was called Design-for-Test. These are large digital logic chips. You want to make sure that when they come out, they have been designed so that test is inherent in the device itself. And there's a couple of mechanisms by which that gets done. Once we complete Design-for-Test, we do the final design review. We push the button and generate the file, that's called GDS2. We generate the file, send it off through Socionext to Taiwan Semiconductor Manufacturing Company, TSMC. I think you're all aware, they're going to be our foundry partner. This product will be built on a 28-nanometer flat logic process. When we talk about [Audio Gap] what really is one of the big benefits of staying in flat logic and not having any esoteric processing that's required is the device with the IP is completely scalable. We're at 28-nanometer, some SoC companies may be in 14. Just divide everything by 2, your power will go down. All of your performance will just scale with whatever the node is that you're going into. And that could be a smaller node or it could be a larger node. Many companies are still operating in a 40-nanometer technology. So while wafers are in the fab, we'll be working on test programs, test hardware, evaluation boards, continued software development and collateral materials. These are all critical parts of the launch process, not necessarily for IP. You don't need their hardware, the test programs will be done by the SoC developer. But with respect to the Akida device launch, having the software in place and the collateral materials done, test programs when the chips come out, test hardware so that you can test them, those are all critical parts of our process. Just a little bit more about the chip. This is just a picture to give you some sense of kind of where packaging has developed over the last 10 or 15 years. Again, this is a complete network on a chip. So there's really no external devices that are required. We'll look at the block diagram in a minute. That means it's got on-chip training. It does on-chip inference. And maybe most importantly is, frankly, we've seen no other solution that can benefit from incremental learning. That is, once you've trained the network and you've got 100 classifiers or 1,000 classifiers, these are the things you want to identify in a video stream or from a LiDAR feed, the point cloud. If you want to add face 101 or object 101, 1,001, you don't have to go back and retrain the entire network [Audio Gap] incremental learning and they add new classifiers in the field at the edge. That's a tremendous benefit to smart cam, smart home applications, even in ADAS, when you've got things that you want to add to that host of classifiers that you're trying to identify in an ADAS system. The device will be available in a Flip-Chip Ball Grid Array. This is -- the packaging technology has been around for quite a while now. But basically, you put bumps on the die itself. You flip the die over, you put it on a substrate, and then you bring out what are called balls that would be soldered to the PC board. So tried and true technology. The device will be 15 millimeters by 15 millimeters square. So it's really quite compact, given all of the -- what's included here. Again, no external CPU required, no external memory required, everything on chip. This is the device itself. You've seen this picture before. I'm going through this because this is all of the work that was done during this past year. The device, as I said, has an M-class CPU on it that's not doing the network, that is basically doing housekeeping. When you look at the big blue box at the bottom with kind of like a checkerboard, that is the neural fabric. That's 80 cores organized as 20 nodes, it's all mesh networked. If you look to the left, you see the green box. That is a great deal of the innovation and that intellectual property, which we need to protect. That's the ability to take regular data, whether it's from a camera, it could be Internet traffic for cybersecurity, it could be LiDAR, radar, ultrasound, could be flat audio data for keyword spotting, but turning that data into spikes so that we can take advantage and the customer can benefit from what an event-based or spiking neural network really does, which is plays off of sparsity, much of the data that comes through is [Audio Gap] why I process it. That is one of the mechanisms by which Akida accomplishes extremely low power. We do have, on the right-hand side, an external interface for external memory, the low-power DDR4. That would allow you to -- if you have an extremely large network that would not necessarily fit on 80 cores, you get 2 routes. You can use external memory and augment what is on chip or the lower blue box is a high-speed [Audio Gap] chip interface where you can use multiple Akida chips. We've currently tagged this at 64. There was a question, we thought about 1,024 previously. In practical terms, 64 seems to be the right number for this architecture. But you can gang these chips together. You need no additional overhead. They basically look like 1 large neural fabric. We'll touch in a moment some of the benchmarks of performance that we've recorded and shared with customers, and customers have validated all the interfaces at the top were industry standard 3.0, PCIe 2.1, I2S for audio, I3C for sensor inputs, maybe pressure, temperature, flow, vibration, any real-world phenomenon that you want to acquire and then provide analytics at the edge. So that's what the device looks like. This will give you some sense of really what is impressing customers -- potential customers, I should say. To the left, you've got your standard -- I mean these are very sophisticated, but I'll call them standard data-based convolutional neural networks, CNNs, they've been around a long time. They kind of dominate the landscape now. They tend to be big players in the hyperscale or data center arena. These are very, very difficult to do at the edge. If you look down the list, you'll see you need an external CPU, you need external loop memory, and you probably need a math accelerator to keep up with all the matrix multiplication that's necessary. These things can be 20 layers deep, 50 layers deep. Some are very, very complex networks. I'll show you some benchmarks in just a moment. It's very math intensive, max or multiplier accumulators. It's basically very, very high-speed math, millions and millions and millions of calculations, and they tend to be relatively inefficient. If you're using a GPU, you could be in a category of 40 to 100 watts. That's far too much power to put in an edge device. The middle section is really where we're seeing a lot of activity right now. This is an event-based convolution. We can do convolutional networks on Akida. We do turn the data into spikes. So we operate in the event-based domain, which gives us the benefit of playing off of sparsity and all the other things that we can accomplish in eventually a native spiking neural network. But you can see it's fully integrated: no CPU, no external memory, nor does it require an external accelerator because we're not doing all of that matrix multiplication. Again, we're implementing the same convolutional neural networks here, but in the event domain, so this could be 20 to 50 layers deep, but we'd get to play off the sparsity of data, which is less operations, therefore, less power. And you can see the last bullet is maybe the most impressive, efficient power. That's 50 microwatts, that's 50 millionths of a watt up to maybe 4 watts. Compared to what you see in the database convolutional neural network, this can go in edge devices, can go in a battery-operated device. And then the third category is when you are truly native spiking neural network-oriented. Similar attributes: no CPU and no external memory or accelerator, but the networks are shallow. They're 2 to 5 layers deep, so you get better latency. You don't have to go through all of the layers to get your answer. We do play off of sparsity as well. And you can see 50 microwatts to 2 watts. That's 50 millionths of a watt to 2 watts. The diagrams below just show you what it would take to do a standard CNN. You've got several devices that you need, takes up space, sucks up a lot of power. And you can see with Akida, once we get the preprocessed data, the device stands alone, needs no external support. These are some benchmarks that we share with potential customers and potential customers that are running the ADE are doing their own validation. These are ranked from lowest power application to maybe some of the larger networks. If you look at the network configuration itself, keyword spotting, on, off, up, down, the ability to provide personalized keywording, keyword spotting, so that you can personalize the device. It's 38,000 parameters, that should be parameters next to 38,000. 38,000 parameters, it's the Google data set of commands. We can do -- we call it frames per second because that's been an industry standard term. A frame implies that you're looking at an image, which is actually how keyword spotting is done. But nonetheless, that's also inferences per second. So frames per second or inferences per second. So you can identify 7 inferences, 7 classifiers and keyword per second. The center block and this is for the more technical guys who also sent in questions, this is the input data size. So you'll have 10 by 10, the last is really what would be color. In this case, of course, it's 1. And then as you move down, you can see that's -- you move over, you can see that's 150 microwatts to do keyword spotting. That's a very impressive number. Object detection, it's not classification, it's just detecting that an object is there. On a proprietary data set, we're running 5 inferences per second. You can see what the input data size is, the number of classes that you're trying to identify and you're running accuracy at 90%, 200 microwatts. I'm not going to go through the whole data set -- excuse me, the whole part here. But if you look at the last row, this is a very, very large network. It's called complex YOLO, You Look Only Once, which is what YOLO stands for. It's 50 million parameters compared to the first line, which was 38,000 parameters, yet we can accomplish 50 million parameters with inferences or frames per second at 133, a relatively large size input data, accuracy, which at 65% is about the best you're going to get with any convolutional neural network that's been implemented. And we can do that with 4 watts, not 40 watts and not 100 watts. So these are the things that are exciting for us to be introducing to customers and editors. Customers are responding very well. Potential customers are responding very well. A little [Audio Gap] about licensing intellectual property. And again, this is the device on the left, the neural fabric and the data to spike converters is really what gets licensed. Builders of SoCs don't need our CPU complex. They're going to have to handle all of their own housekeeping. They'll pick whatever faces their device has. So really, what they license are the cores, and they license the data to spike converter or converters. Now they can take all 80 cores, which is 20 nodes, or for keyword spotting or other similar applications with less parameters, they may only want for 4 nodes, which are going to be 16 cores. But that is up to the customer and we will work with them to determine what is the size of their neural fabric to complete whatever tasks they have. Additionally, what customers -- potential customers see as valuable is you can run multiple networks. So if you took all 80 cores, you could dedicate 10 of those cores, or do them as nodes, take 20 nodes, then you could take 3 or 4 or 5 of them, and you could run 1 network to do object detection, you could take the other cores and have them do keyword spotting or some other network. But again, it's complete. It's on the chip. You're not running the network on a host CPU. So you can basically run multiple networks on the device simultaneously. Talk about intellectual property licenses a bit more. There were a lot of questions about it. And I think, in part, that's because we've -- we voiced a strong opinion that come in advance of actual device sales. There's no manufacturing process involved. There's no inventory. There's no long package qualification by the customer. So we released that in 2019. We have received strong response from prospective customers. The ADE in the hands of 1 major South Korean company is being exercised, almost as much as we exercise it. They really have dug in, validated some of the benchmark results that we provided. Now they're moving on to some of their own proprietary networks to do validation. We've targeted specifically vision and acoustic systems. Those are 2 places where, at the edge, there's a dominance of requirements. We also have cybersecurity working in the background as well. That is a native SNN. That's not an event-based CNN. But in vision, we can do event-based CNN. In vision and acoustic, we can do event-based CNN and work toward developing, in collaboration with customers, potential customers, we can work toward moving them into a native SNN environment. Certainly, there's a lot of activity in the automotive industry. I got asked questions about what's going on with companies like Volkswagen and Bosch. Really, the -- I think the shortest path to success is working with -- certainly working with the major automobile manufacturer, so they put some top-down pressure. But when you look at companies like Bosch; company out of France and Germany, Valeo; Continental here in the U.S.; Aptiv, which was formerly Delphi; ZF; and several others, they build modules that they send -- then sell to the automotive market. Now some of these are big companies. Valeo is a EUR 20 billion a year revenue company. Continental and Aptiv have to be in the same kind of category. These are primarily going to be radar, LiDAR and cameras, maybe some ultrasound and cameras for ADAS. Too early at this juncture, Level 1, 2 and 3, which is ADAS. And certainly, with the target being autonomous vehicle is Level 4 and Level 5. In other vision applications, vision and acoustic, smart cameras, smart home systems, a plethora of edge use cases. Here, again, you have the OEM manufacturers the guys that build the cameras themselves and sometimes build entire systems, or you can work with Tier 1 sensor manufacturers, which want to incorporate incremental intellectual property into their device, so they get more of the gross margin dollars. And here, the leaders in this space are Sony, ON Semiconductor and OmniVision. Amongst those, who probably have all the cellphones in the world, at least the vast majority of them as well as smart cameras. So partnering with the image sensor guys as well as module manufacturers in the automotive industry, we've taken a very, I think, diligent course in identifying in each of these marketplaces who are the most likely to be successful: who has won market share; what do their future road maps look like; how are our existing relationships with those customers; and as important is intersecting their design cycle at the right time. So for us to generate near-term revenue, it can't be a company that has just released its last generation module and is on a 2- or 3-year path to identifying and building their next. So intersecting at the right time, and I think we've been fortunate in that regard as well. Acoustic applications for smart homes include a lot of Tier 1 suppliers in the U.S., in Europe and in China. And I'll touch on China a bit more here; and China for cameras, hubs of all kinds and peripherals. I just came back from Shanghai, Roger and I were in Shanghai, I guess it was 1.5 weeks ago. It is -- it's an incredible amount of energy, an incredible amount of financing going into AI generally, more specifically AI at the edge. So we'll touch on what our plans are in China in just a few moments. So in order to really attack the IP sale, it's a different selling process than selling chips. So we have retained a group called Surround HD, 3 guys we know very well. Very, very seasoned executives in the IP sales process, relationships with all of the Tier 1 suppliers. They are really -- with myself, Roger, Anil, maybe we can have his attention, Peter, we're really the face to the customer for IP sales at this point. As we release the device itself, we'll have a more traditional semiconductor sales force with manufacturers reps in the U.S., distributors overseas and, in some cases, there'll be global distributors as well. In most markets in Europe, there are regional distributors that are technically competent. Someone asked a question about our relationship with a company in Israel called Eastronics. Eastronics is a very, very technical -- they call themselves a distributor because they do inventory product, but they really act as your manufacturer's rep on the round. We're just reducing to practice a contract to get it done. They've already started to work with us in the field and introduce customers. Somewhere along the line, one of your folks that does all this great diligence stumbled upon the fact that they've already started to promote our product, but that's moving well and that's in Roger's hands. So in Europe, with respect to IP, we've got an existing relationship with T2M. They're bringing us great opportunities. They are a major independent supplier of IP. So they don't build SoCs, they basically market blocks of IP to their account base, which tend to be people that are building ASICs or system-on-chips. We're also in discussions in Japan. There was a question about our relationship with Socionext. We'll touch a little more on -- I should do this now. Again, the manufacturing or the development process has gone exceptionally well; very, very strong team, both in San Jose here in California as well as in Shin-Yokohama in Japan. We couldn't be happier. Our team works very, very well with them. But we have had significant discussions about how to broaden that relationship now that we're going to the wafer fab. They build ASICs. They're the second largest ASIC supplier in the world, only behind Broadcom. They would be a phenomenal channel for us to have our IP block in their menu of alternatives that they can present to their customers for neural network embedded in SoCs or ASICs. Having similar discussions more with design services than ASIC houses. The design services in China is a very big and well-worn path. Again, they would license IP, they would market to their customers, and some of those customers would ask them to build the ASIC or some of those customers would have their own in-house SoC capabilities, and they would market the IP into China for us. I already talked about Israel. China's going to be an interesting place. We've applied for obtaining an export license. We may or may not need one in that path. Like every government agency, we don't control the ball. We're certainly on the field just trying to get this done. But Roger is in -- the point person on this. We've had interrogatories go back and forth, but we certainly will need to have clearance that we don't need a license or if we do need an export restriction license, what the type is and how we deal with it. But China does represent a very large component of the AI edge applications. When you think about smart cameras, there's a couple of companies in Hangzhou that really dominate the space. They've got an extremely large market share. And they have their own national ADAS and autonomous vehicle developments as well as vision-guided robotics. That's a market that we certainly can't ignore, and we've taken first major steps to figure out how to develop that presence in China. So let's talk about a couple of the lowlights because I don't think we should ignore them. As you know, BrainChip Studio, our end-user effort, proved to be far too expensive, wasn't really scalable and we've pulled back to only dealing with OEM engagements. We've got a few that are continuing to move along. And again, these will be cloud-based BrainChip Studio opportunities, where facial recognition, object detection, object classification. So we haven't given up on BrainChip Studio. But again, as you probably are aware, fundamentally, most of our resources, if not all of our resources, are focused on the Akida development, introduction, sales and marketing. That is what this company, from inception, has had as its core mission. BrainChip Gaming. I'm sure everybody is aware that GPI was acquired by Angel Playing Cards. We have signed a distribution agreement with them. If we ever chose to adapt what we have built to meet their system requirements, and that's not on the front burner right now, as I said, we're dedicating virtually all of our resources to the Akida development and introduction, but they're going through their pains of integration. And when you put 2 large companies together and we've got thousands of employees with overlapping duties, they've got a lot of rationalization to do on their part as to where they want to play and their kind of vision system approach to the gaming industry as well as what they do with their own teams and where their manufacturing is going to be. So I just wanted to comment that we have a relationship. I was in Kyoto with their management about a month ago. But again, all of our resources are really kind of behind getting Akida into the marketplace. Quick financial update. We haven't published December numbers yet [Audio Gap] working that out now. So this is a dated number. We finished with $9.5 million in cash in September. We did provide a forecast as to what we thought our operating -- steady state operating expenses would be for the quarter. We have initiated significant reductions in planned expenses. And what I mean by planned expenses, what we have in the way of head count, what we have in the way of expenses to support the development and introduction of Akida, they're all baked into our financial forecast. But we did have plans in the beginning of 2020, maybe January through June, to do a rather large expansion in sales presence, feet on the street as well as some other areas. We have restricted all of our hiring now to really essential personnel to get Akida into the marketplace, to start to promote more actively. And I think we'll touch on this in a moment, we'll be far more active in promotion with more standard press releases out going over wire services to reach customers and editors. It is becoming more and more difficult to having anything that looks like a market release get lodged with the ASX. They've put some significant restrictions there. So I have appended to this presentation the last 6 months press releases that really highlight the activity that we've had in the last 6 months. I know it seems a little anemic on the ASX but there has been a great deal of activity in promoting Akida, the intellectual property, getting the message out, attending shows. We did the workshop in Western Australia a few months back, which was very successful. So again, really significant reductions in planned future expenses until such time that we get traction. Licensing deals that could help offset costs as we go forward. And then we'll make prudent decisions about head count expansion and other supporting functions. The financial outlook, this is -- there's not much that we can say publicly. But certainly, intellectual property licensing is expected based on current and future customer engagements. There's been a lot -- great deal of activity. As I've said, we've got our target list. We're in the field all the time. I think I spent probably 6 of the last 8 weeks out of the country, or traveling across country here. It can -- as I've said, it can proceed, and likely will proceed, Akida device sales, because there is no manufacturing. Licenses are prepaid, particularly if you're going through an ASIC supplier or you're going through design services house [indiscernible] market the IP. They prepay, it's all well documented. And maybe as important as anything, intellectual property represents significant gross margin because there's fundamentally no cost of goods. Most of that falls straight through to operating margin. There's not a lot of operating expenses, maybe sales commission and maybe attributing some overhead to the cost of goods sold line and hitting gross margin. So I think the IP business, the IP licensing can certainly add support to the company's cash requirements in 2020. Over the course of the next couple of months, we'll be able to size that far more effectively and we'll comment on it. I do intend to do these webinars on a quarterly basis, as we had done in 2018, just so we can keep everybody up to date, particularly with some of the restrictions that we have in the ASX. We want to make sure that -- anyone that wants access to the press release content, you certainly can register for our direct e-mail service. So you won't have to hunt and peck to find stuff. Although I think this community keeps a pretty good eye on the ball, but you can certainly register for our direct e-mail and then you'll get sent all that information in real time. So there was a question about design wins. And a key of being able to be used and handled by customers based on engineering samples, as you go through what's a pretty arduous process on the back end of doing split lots and when you do temperature testing, all of those things, but design wins certainly can be accomplished on engineering samples. Those engineering samples, as I said, we go into fab sometime here late in January. We'll see what TSMC comes back with, with respect to lead time, it's quite variable, but the mechanism that we're using should put it on a hot lot status and move as quickly as possible through fab. And OEM customers themselves, once they have the device in their hands, their design cycles are variable, right? You work with Cisco, and they're building a new router. Again, you have to intersect at the right time, but that can be quick on the process. You intersect with a smart camera manufacturer in China, that could be a matter of months, and they've got production units in the field. So it does vary somewhere 6 to 9 months on the short side. Automotive applications, large networking applications certainly can be far longer and could actually stretch it to several years. We're evaluating opening 2 innovation centers and a software design center, so these are expenses that we will be prudent about. Some are baked into the current forecast. Some we'll absorb as we move throughout time to get these centers opened. The software development center will be in Hyderabad, India. We've already got 5 contractors working there doing a great job. We control the infrastructure so we can control our intellectual property exposure. We're looking at an innovation center in Western Australia where we've had a significant historical presence, there's a good talented engineering community, and that would really be focused on advanced research and development. We have research that we call applied research, which are things that Peter and company have been working on, are reasonably well-proven and now were being applied to the Akida design, and you have advanced research, where Peter has already got a stack of paperwork that he's generated in ideas about what Akida 2.0 would and should look like. And certainly, if we're going to have a presence in China, we will have to have some local engineering support. So a small innovation center in China is something we're also evaluating. Need to have local language Chinese. We need to have collateral materials in Chinese, probably simplified Chinese. But there is such a large and growing AI marketplace there that it's certainly one we can't ignore. I'm not going to go through the company announcements. I've put them here for your convenience. And I think you can see there's been a great deal of activity. We certainly have taken a bigger presence in utilizing our LinkedIn service. We've certainly taken a bigger presence using Twitter. So you can follow us on LinkedIn. You can follow us on Twitter. You can register for the direct e-mail campaign. I'm going to touch on the first one, which just came out in December because someone asked the question. Tata Consulting Services, I'm sure most of you all know that Tata is a very large Indian-based company. I think it's a bit over $100 billion in annual sales. This was an interesting application because this was a native spiking neural network. Tata, this was a robotics group, there is, and we've talked about this in the past, there is a new technology for image sensing called DVS, which is dynamic vision system or dynamic video. And it is a superb application to demonstrate how direct spikes into a key that can be processed very, very quickly. It could be hand gesture, it could be a motion in a robotic system. What the future holds with us on Tata, we really don't know. We know it was a successful demonstration, and we'll continue to work that. There is -- on the October 31 release, if you haven't read it, I think it's a good report. Linley -- The Linley Group microprocessor report is very, very well respected. And if you read this particular piece, and I think it speaks very highly of Akida and its capabilities, that Linley report is read by a very, very large audience of engineers and editors. The patent that was awarded, we talked about patents, again, they're provisional patents, will be reduced to practice as utility patents beginning of this year, and those applications will be made. I think I touched on the development workshop, we will do others. And I think there was, I don't know, 25 people-ish, something like that that attended, it was really well done. And I think we got to -- we got high marks from the audience. I think -- frankly, I think that covers everything that I wanted to. I believe I caught most all of the questions. I tried to group them together because there's some overlap. But I guess that's it for now. And we will speak again after the quarter. We'll do a quarter update, and we'll keep this process going. So thank you all for joining us, and hope you have a nice day.