Episode Transcript
[00:00:03] Speaker A: Welcome to AINA Insights, where prominent leaders and influencers shaping the industrial and industrial technology sector discuss topics that are critical for executives, boards and investors. INA Insights is brought to you by INA AI, a firm focused on working with industrial companies to make them unrivaled segment of ONE leaders. To learn more about INA AI, please visit our website at www.aina.AI.
[00:00:40] Speaker B: Welcome to another episode of Titanium Economy podcast hosted by aina. Today we are joined by Prith Banerjee, SVP of Innovation at Synopsys.
Synopsys is the backbone of modern chip design. Virtually every advanced semiconductor company relies on its EDA software to turn design into silicon. With the 35 billion ANSYS acquisition now complete and a 2 billion strategic partnership with Nvidia underway, Synopsys is expanding from chip design into full silicon to system engineering.
Prith helps foster and lead the company's technology strategy across eda, simulation and ip. Previously he was at Ansys where he was cto and before that he has had senior leadership positions at Schneider Electric, abb, Accenture and hp. Earlier in his career he was Dean of engineering at UIC and chaired the ECE department at Northwestern. He's authored over 350 technical papers and founded two EDA startups. Prith, great to have you with us today.
[00:01:44] Speaker C: Great to be on this podcast with you Prith, and looking forward to it.
[00:01:49] Speaker B: Perfect.
So Prith, one of the things first things that I want to start with is dozens of people use devices every day that are powered by chips, but very few know about Synopsys. Can you tell us a little bit about what Synopsys does and why does it sit at such a critical point in the technology world today?
[00:02:07] Speaker C: Yes, so what?
Synopsys is a leading provider of electronic design automation tools, IP and simulation.
When you have a chip from either Nvidia, Intel, Qualcomm, Broadcom, those chips are designed using electronic design automation tools from Synopsys.
And then these large chips have significant intellectual property blocks in them. Blocks like PCI Express or Ethernet controllers or memory controllers. Those are also provided by Synopsys. And then with the latest acquisition of Ansys, a leading simulation provider, we now have the full chip to systems solution from Synopsys.
[00:02:51] Speaker B: Perfect. Thanks very much for that introduction and very helpful.
Now one of the exciting this is an exciting year for Synopsys. I think December marks the 40th anniversary for Synopsys and you look back at the journey of for synopsis, what has most fundamentally changed about what Synopsys does and the role it plays and Also, as you talk about re engineering engineering, what does it mean for the next phase of growth for Synopsys?
[00:03:21] Speaker C: Actually that's an interesting question. You mentioned my bio. I used to be a professor, so I'll actually start at the beginning.
I started as a professor at the University of Illinois, Urbana Champaign, teaching VLSI design. This is how do you actually teach students how to design VLSI chips using Carver Mead's book on VLSI design. At that point it was all custom IC design.
I taught students to actually draw rectangles on a screen. Those rectangles represent masks. And so you have a polysilicon mask over a diffusion mask that implements a transistor. That's how chips used to be designed in the 80s.
But custom IC design had its limitations. You could only design maybe a human design. I could do design maybe 10,000 rectangles, 100,000 rectangles, so about 10,000 transistors.
That industry took a big move through the use of what is called standard cell design where you pre design cells for a 2 input NAND gate, 4 input NOR gate, et cetera, small, medium, large versions.
And so instead of drawing rectangles, designers would draw gate level netlists and gate connected with nor gate and so on. And then when I started teaching VLSI design, I was teaching it at that higher level.
And so the electronic design orientation tools, the EDA industry moved towards placement and routing of those cells on a chip.
Then came the next big deal from actually by Synopsys, which is RTL synthesis where you enter a design at a register transfer level verilog or vhdl. And then the design compiler, this synthesis tool generated the. Net list of gates and then you did the placement routing to do the rectangles. So something that a human designer could do with 100,000 transistors could now you could do a million transistors with placement and routing now with 10 million transistors and so on. So this whole industry was significantly impacted with the invention of RTL synthesis. And that was the beginning of Synopsys. And I was part of the journey. I used to be a professor coming to the ic, CAD and DAC conferences. And I met Art, the founder, and Alberto San Giovanni. So these are two of the founders that I have known for many, many years.
And so this has been an incredible journey. But then the whole industry said, okay, I want to do even more complex things, right? So essentially you added more IP blocks, pre designed IP blocks. So instead of doing standard cell gates, you essentially took a memory IP block or an ARM processor IP block or an ethernet controller IP block. So you in a large chip with 100 billion transistors, you may have 10, 15 IP blocks and only a small chip is done through electronic design. So that's essentially what was happening in the chip industry.
But then as the amount of transistors that you could put on a chip got sort of maxed out. Right. Because the size of your chip is fixed, so you can only put that much in it. Right. So essentially people said, hey, how can it put more transistors in a chip? So you say take a small chip, which is a chiplet, and then you put multiple chiplets together on a die and you connect them up through 3D IC and so on. So now that is what is going on. You are jamming more and more transistors inside a multi chip thing with 3D ICs.
So the complexity of that is getting even more complicated. And that's where the ANSYS part comes in. Because with Ansys you can do all the multiphysics simulation for the thermals and electromagnetics and so on, and we'll talk about it. But it has been incredible journey and now people are talking about putting a trillion transistors on a chip or a wafer. The Cerebras chip has I think 2.6 trillion transistors on it. So it's an incredible journey going from the 100,000 rectangles that I used to teach in 1984 to the trillion transistors today.
[00:07:35] Speaker B: Yeah. And I mean, yes, truly. Right. Like the chip designs have come a long way from at least the beginnings that you're talking about, you know, to where we are right now. And I think the, the growth is going to continue, at least for the foreseeable future.
Prith, let's first talk about ansys.
It was one of the biggest tech acquisitions. And it's also deeply personal for you, like you being the CTO there for seven years and then moving to Synopsys.
Talk to me a little bit about what were the strategic rationale behind that particular deal and what does the integrated company now look like now you're on the other side of it.
[00:08:15] Speaker C: Yes.
So the rationale behind the deal is the fact that the intelligent systems that we see around us in any industry, from either automotive, aerospace, healthcare, energy industrials, every one of those industries has. So suppose you're looking at a car.
That car is a software defined car. Right. So there's a piece of some 10 million lines of code that is running on that car, on that Chip to make that car autonomous or Semi autonomous.
That 10 million line of code or 100 million lines of code runs on an ECU. It's a Silicon chip, right?
Designed using EDA tools from Synopsis. But then that chip, that software is doing the autonomous control of a vehicle. That vehicle is electromechanical part, right? It has wheels in it. So, so with ANSYS simulation you can actually model the inertia, the mechanics of it, the external aerodynamics, how much wind, drag there will be, etc. Right. And then if it crashes into a wall, what will be the crashing? So it's electromechanical thing that is going on that is being controlled by the software running on a chip.
So in every industry the vision is IT's software defined AI enabled silicon chip design, right? This is the transformation.
And so to make it successful you need to inside the same design tools. What if you would get the chip design as well as system design in the whole chip two systems.
That was the thesis behind it, right. So earlier Synopsys was in the area of electronic design automation. What's the size of the market? 10 billion? 15 billion.
Now the market is the entire product area.
So collectively across industries the market is like 1.8, $1.3 trillion. That's the R and D budget. That has essentially become the market for these tools to design products in this space. That was a rationale.
So then the transaction was announced in January of 2024. It took 18 months for the transaction to go through with all the regulatory approvals and so on. And we were all excited about it on both sides and lots of integration discussions were happening. But we actually couldn't really do it because until the thing is done, I have now been part of Synopsys, the executive leadership team. And from July we are now the combined company. There's some very, very interesting joint solutions that are being built together, right? So for example, if you have sort of this multi chip, the 3D IC that I was talking about, right? You put multiple chiplets on a, on a chip and essentially you're trying to find out what the thermal behavior will be, what the, the emi, the electromechan, the EMI EMC will be, right? We have electromagnetic tools from ansys, HFSS that can do really detailed electromagnetic interactions. Or we had power tools from Ansys, which is Red Hawk. So the Red Hawk can be integrated with the EDA tools, prime time and so on, right? To do this. So we have done some absolutely phenomenal joint solutions work that is going on and our Current customers are loving those joint solutions.
[00:11:42] Speaker B: I'm pretty sure there's a lot to come on that front as well. And I really like one thing that you talked about, right, It's a software enabled AI driven chip design. And we're going to talk a little bit about that because that's a critical point.
But talking to the other partnership that's going on right now is with Nvidia investing $2 billion in Synopsys on a multi year partnership for a AI driven design as well as on the digital twins.
What does this partnership enable or unlock that wasn't possible before?
[00:12:12] Speaker C: It's a great question actually.
I need to explain to you why this kind of computational power is needed. So when you look at an electronic design automation tool like say Fusion Compiler that automatically synthesizes a design from RTL all the way to placement, routing or prime time, which does the sort of timing sign up, these computations require a lot of time to run. Or then you look at the ANSYS simulations, fluid simulation. I mean this is partial differential equations. You have to solve them numerically. It can take 1000 hours to run a fluid simulation or a mechanical simulation, doing crash things and so on. So these things require a lot of compute. And so in these areas, when you try to run them fast, right on a well, when your clock frequency of your chip was increasing every two, every 18 months, you are getting double the frequency. You could just wait for the next processor to come in and it will run faster. But as the frequency has plateaued at 2 GHz, you have to rely on something else to make it happen. So the early work was, well, let's just have multiple cores within a CPU and you rely on instruction level parallelism to dynamic scheduling to get speeds up. That kind of took us to maybe two times, three times the speedup. Then you had said, hey, now I have to rewrite my code to take advantage of shared memory parallelism. Meaning I take my code, I say fork a process here, fork another process here, and then you join the processes and then lock unlock. So you're writing programs to do shared memory programming that took you to maybe 15 times, 16 times speedups, it was not scaling to hundreds. So next step was do message passing distributed memory. The reason I'm going to it, this is my research background. This is what I used to work on as an academic, as a professor.
So the message passing part let us go up to multiple things, maybe hundreds of processors.
So both the synopsis part and the ANSYS part of the tools were working on that journey.
But these codes, you could literally take these codes, take an original Fortran code and say, well, I will make a shared memory version or a message passing version. But then the GPUs came around, right?
And we said, wow, this is an interesting thing. GPUs first came around for just to do ray tracing, graphics and so on. But both companies realized these things can be used to do parallel processing, but at a very, very fine grain, like 16,000 cores. Well, you cannot take a normal sequential algorithm with Fortran program and say, oh, I want to run it on 16,000 cores. You have to really rethink how will Fluent be executed using GPUs, right? And a single GPU, multi GPU. So we at Ansys, we are on a journey to take each of our code, fluent HFSS mechanical LSDyna and get them running on GPUs. Same thing at Ansys at the Synopsys end, right? Fusion compiler prime time test max.
So we are on a journey to make this go faster with GPUs. So the partnership with Nvidia is first, let's get this industrial software which synopsis analysis each of these codes running efficiently on GPUs, which will completely change the way you can do things much, much faster.
But then there's this other opportunity of AI.
Each of the tools, it turns out, like for physics simulation, you can use AI to train them and then Fluent powered with AI will run like a thousand times faster. So it's not GPU parallelism, it's just, well, you train the model using our platform like Sim AI or optislang, so much faster.
So what Nvidia noticed is, hey, the world is now being transformed into what is sort of physical AI, which is the world around us is autonomous driving or robotics and so on. And as a robot interacts with its environment, the environment interacts with these not words AS tokens as ChatGPT or images as tokens as Dall E or videos as tokens as in Sora. It's a whole new world. It's world physics models, right? And the physics models you have to create the synthetic data.
Who creates it?
Ansys synopsis so it made a lot of sense for Nvidia to take a look at this combined company, say this is the company that Nvidia should be partnered with to take that industrial software, make it run first of all on GPUs. Secondly, run it with the AI powered stuff and then use that thing to train the next set of autonomous driving vehicles and Robots. So it's like the full circle. It's an incredible partnership and our teams are working so fast, so hard on these joint solutions.
[00:17:15] Speaker B: Yeah, and I know physical AI is becoming so and so more and more relevant and being talked about so much these days. I'm looking forward to seeing some exciting stuff coming out of this partnership over the next coming months and years.
Talking a little bit about this AI enabled chip design. I know on one hand the chip complexity is increasing quite a bit, but also the design cycles are compressing.
How is AI changing the end to end design workflow?
And from your point of view, where does the human judgment still matter the most?
[00:17:48] Speaker C: That is a great question. And again, let me share with your audience the complexity that is changing. So there was a time when you had maybe a million transistors on a chip, then became 100 million, then a billion, now it's 100 billion going to a trillion transistors on a chip, on the chip itself. But now look at the intelligent product.
That chip is running a 10 million line software code and that software code is running on this chip that is controlling all these airplane wings and so on. So you look at any product in any industry. A car used to be designed. It took like six years or four years to design a car. And that you now need to design a car in two years or a one year, a chip that took maybe three years to design.
Now you need, I mean, Jensen asked for Nvidia to make a chip every year. So that type the time to innovation, right? The next product is decreasing and the complexity of the chip or the system has gone from 1 million to 10 million to 100 million to a billion, right? So the complexity is increasing and the time is getting squished.
So if a human engineer could say, do, I mean, just think about laying bricks on a wall, right? A human can lay 1,000 bricks in a given year, right?
So now that same engineer has to do, instead of 1,000 bricks, 10 billion bricks. So you need like thousands, millions of engineers to do the job. That is not practical. So what we have done is to say, hey, can AI help? So basically all the stuff that a human engineer was doing is can I do it with AI? So the first step was we took each of our EDA tools and ANSYS tools and we put AI inside those tools, right? What we call machine learning enabled, right? So you took fluent powered AI or primetime powered AI. Then we said, let's take the whole stack of optimization. When you are running a chip design or whatever, you are actually Doing various optimization things. And you're saying, if I tried this, this is going to happen. If I tried that, that is going to happen. So it turns out this is the whole world of what is called reinforcement learning. And you design. So the early work was on design space exploration.
When you're doing digital design, you're doing this, turning this 15 different knobs in all these different directions.
And just like when you teach a child learn learning how to ride a bike, you go left and you fall down, that's a penalty. You ride well, that's a reward. So you give a reward and a penalty. Use the reinforcement learning. And that is the core of how we did dso.
Once that worked for digital design, they said, okay, can it work for verification? That was vso.
Then we said, does it work for test? That was tso. So essentially we took the entire set of suite of EDA tools, dso, vso, eso, et cetera, and we made all those things.
The next step in the journey was oh, the human designer. Vineet, you're doing this. You are actually, you need to create a test bench. Can I have an assistive agent that will help me in creating the test bench? Can I create the test benches? So essentially now these agents are assistive agents or creative agents. And we have made a lot of things in the journey. The next step, which is the future, is all these things. Agents will work among themselves in a multi agentic workflow and do the job of an entire team of engineers, full automation. So just like in an autonomous driving, you have level one driving, which is you're assisting the driver to do cruise control, right. Or level two is sort of automatic parking. Right. And level five is fully autonomous driving. Right. With these Waymo cars in San Francisco, we are imagining a world where the complete process of a chip design, of this complexity, of this trillion transistor chips in a year, less than a year will be done with these agentic engineers.
So we think we are excited, we have just talked about it in our Converge conference about re engineering. Engineering. So how the process of engineering these complex chips to systems will be re engineered with these agent engineers working hand in hand with human engineers.
[00:22:10] Speaker B: That's a very fascinating transition that you're talking about on one end where AI was just assisting the engineers on the design part to now these creative agents that are not only generating but also evaluating their own designs that you're talking about.
That's a pretty far along we have come on that particular journey. I'm pretty sure there are a lot more advances to, to come in that one quick question. How does it feel like when machine proposes something that a human engineer hasn't considered before?
[00:22:40] Speaker C: This is actually a very, very interesting thing. I mean, I'll give you an example within the sort of 3D CAD computer aided design area, right?
So a human engineer working for a car company would say, here is the design of the car. They will actually design the car shape on a CAD tool.
And then they would use the ANSYS simulation to simulate how the external fluid flow dynamics happens. Or they will bang the car virtually to see what happens. And if you don't like the design, so you did a cad, the human designer did the cad.
Then the simulation evaluated what that CAD does. Right. And if you don't like it, the human designer went in and changed the cad.
Oh, now let's remove the mirror from here so there is less drag.
Then you do a simulation. Oh, I liked it. It turns out AI is now saying, hey Prith, you did the CAD this way. Let me do a CAD in a slightly different way. So we have a tool called geomai that makes suggestions to the changes in the geometry based on the objectives that you have set up. So there are literally thousands of different geometries that are being generated by GMAI that are being simulated very fast with SIM AI. So it's this GEOM AI generates the geometries and SIM AI simulates the things. And it's this combination of multi agents that is generating these really, really unique things.
Sometimes this geometric AI creates designs that a human designer has not thought of because a human designer gets tired. They only work eight hours a day. This thing works 24, seven non stop, right? And it searches. It's like playing chess, right? When you're playing chess, a human can only think about two moves down. A computer can go like 28, I mean eight moves down, right. It has tremendous resources to explore. That is the power of AI.
[00:24:36] Speaker B: That's very fascinating.
Perfect. Let's switch gears a little bit.
And one of the topics I know that is deep to your heart is the digital twins. And one of the key rationale for the ANSYS thesis was also around the digital twins.
[00:24:51] Speaker C: Right.
[00:24:52] Speaker B: Where is this being used in the industrial applications today? And what does the roadmap look like for this?
[00:24:59] Speaker C: That's another fantastic question. Again, when you introduce my background, you kind of talked about the fact that I was CTO at ABB and Schneider Electric. These are two large industrial companies.
And in that role as a cto, I was responsible for creating a digital twin of an ABB robot or a digital twin of a Schneider electric breaker or a power supply.
So these are large assets and you put these assets in the field and if it fails, you have to replace them. So this is a really challenging thing. So how do you service these things? So, so what these companies did, and abb, Schneider, ge, all of them wanted to build a digital twin model. Here is a physical asset. You make a model of the asset and then you essentially you put sensors, IoT sensors on these assets and you pull data from the sensor. And based on those signals that you're pulling, you're saying you're predicting will that transformer fails. So because if you can predict that the transformer will fail tomorrow, you can replace it today and avoid the downtime, right? I mean we had a power outage yesterday, right? For three hours we didn't have power. So could that have been prevented? Absolute can, right? So this is.
So people have talked about digital twins in automotive, in aerospace, in energy, in healthcare, every industry.
So here I was as CTO at ABB Schneider, I said, how should we do it? So the early work was you pull data from these assets and these are like time series data. Based on the data you can do a predictive analytics. The accuracy of that AI based data driven analytics is about 70%.
So that's pretty good. But if you have a million dollar part and it's only 70% accurate, then 30% of the time you're making a mistake. So you made a decision to replace a million dollar transformer with 30% error means you lost $300,000 on that wrong decision. So I always wanted to say, can I do better? So I landed up at Ansys. I said, what if we could combine the physics based simulation with that thing? And sure enough, we actually did that.
With the Ansys twin builder solution powered by AI called twin AI, we are able to increase the accuracy of it to 99%. This million dollar transformer now with all this stimulation, because it actually knows how the transformer is operating in the real condition and so on. So this is what is called digital twin in the operational domain. Now at Synopsys we are saying, couldn't we do the same thing with a digital twin of an as designed thing, right? So this vision that I shared about a software design defined vehicle, you are writing a software, 10 million lines of software. The software team is writing the software on a chip that is still not yet done, but the design of the chip is being designed by the chip engineers and they have a Verilog model of that chip. Verilog is this language.
So Synopsys has a tool called the virtualizer in silver which allows you to create electronic digital twin of this automobile that you're doing right now. You're writing the software. That software is 100 million lines of code. You have the Verilog implementation of the chip. We have a platform called Zebu and HAPS that can actually emulate that 10 million lines of code running on the chip when it will be fabbed. Right. So these two designers are working on it.
So that was the electronic digital twin. Now what we are working on is that software is going to control the vehicle. That vehicle has aerodynamics, that vehicle has crash all the mechanical part. That is Ansys. So you take the Ansys simulation, use digital twins that we have from Ansys, connect it together.
That is a digital twin of that product before that car is designed and manufactured, before that plane is manufactured, before that pacemaker is done, before the wind farm is done.
How cool is that?
[00:29:13] Speaker B: That's very fascinating. A quick follow up on that one.
It's a very fascinating technology, right. And there's a lot that you can predict ahead of time to save you, like the design cycle time, the production time and all of that.
Plus I could assume, right, like you can enhance the safety and the reliability of the products that you're designing and operating with this. But do you think that the industrial organizations are ready to take this kind of a technology now or what is holding them back?
[00:29:42] Speaker C: So I am talking to, I just said I was talking to a CTO of FA customer today, talking about precisely this thing. So I mean in my role as SVP innovation, this is what I do. I reach out to our customers, talk to a cto, says, hey, can we help? And so every time I talk about this thing, it resonates. We actually talked to a customer about using digital Twin in the manufacturing of this customer. They're going to use it. So I am super excited.
What happens in this area is bottom up, the engineer on the ground doesn't want to touch it because it's going to impact the way this is going to happen. Right? So this is a conversation. These are always. This is how I am trying to influence our customers. You go and share the vision and the value of this at the top where with the CTO and the CEO of the companies and they say, oh, this is the real value. So then essentially that drives in this customer that I talked about today morning. It is now going to drive down to the organization and they will Say, hey now, let us be open to testing this thing out. Does this twin AI technology work, this digital twin with automobile, does it work or not? And I am very confident this is going to work.
[00:30:55] Speaker B: Fascinating.
Let's talk a little bit about you.
You have had a very sort of a genuinely unusual career track, from academic dean to an EDA entrepreneur and to a cto and now back into a deep tech. What is the through line that connects all of these?
[00:31:16] Speaker C: The thing that connects it is called innovation. I mean, I have always been interested in the topic of innovation. Innovation is basically what you would.
It's something new that the world has not known before and it provides some value, either economic value or a societal value. That is how I define innovation Now. I was practicing innovation in academia, doing research, fundamental things, proving theorems, doing the et cetera, working with PhD students. Had more than 350 papers and 35 PhD students. So that was the academic part, but those were adding to the knowledge. Research in the field, but not actual products.
Then I did my two startups and we literally take their ideas from their academic lab. And the two startups were actually companies that took those things, raised money from VCs, built a product, sold their product. Those were really innovative products.
And then in the Last sort of 15, 17 years, I worked in large companies like HP Labs and Abbas and so on. And these large companies, we are always trying to innovate, come up with brand new things and so on. But what I have found is these large companies, they do the incremental innovation or horizon one innovation. You used to work at McKinsey. McKinsey actually coined the term horizon one, two and three innovation. And I borrowed that and I wrote a book called Innovation Factory on it, saying that Horizon 1 innovation, big companies do a great job. Horizon 2 adjacencies, they also do a good job. But large companies struggle with the horizon three, the disruptive innovation. Except companies like the companies that you know of, Apple and Amazon and so on, right? So the question is, how can large companies like Synopsys, Ansys, abb, Schneider, HP do disruptive long term innovation in a methodic way, right? That is what I call in my book Open innovation. Working with academia and startups, you have to have an organic team in your company that is pushing the boundaries of stuff. But they should not all do it themselves. They have to do it in partnership with academia, in partnership with startups.
Innovation has been the common theme across my team and that is what I am absolutely enjoying at Synopsys.
[00:33:32] Speaker B: That's fascinating.
Let's take one of those situations. I know on a leave at Northwestern you founded Excel Chip with your graduate students.
What does building something on your own teaches you that no other corporate role can?
[00:33:48] Speaker C: So I can literally do a one hour podcast on that experience.
The simple thing is I did Excel Chip. I founded Accel Chip because here was a thing. I just finished a project from DARPA sponsored Match compiler project because we showed darpa here is you can take a matlab version of a signal processing, whatever, algorithm, networking algorithm and you can design a chip with an fpga. And in our program I just said this is so good, you should transfer it to technology. That is what DARPA wants. So I went around, came to all the companies and I said, oh yeah, sure we'll use that technology. But I could see the look on the engineer side. They will just take it. Nothing's going to happen unless I did it myself.
So I took leave from the university, I said I'm going to do it and I wrote a business plan.
And boy, that was another thing, right? I mean trying to write a business plan, getting funding is not easy as an academic person. But I kept at it, raised money, $2 million from a VC and then formed a team. The team had no experience about building a new product. It was all my graduate students. So we kind of made a lot of mistakes. But ultimately we managed to build a product that actually sold. I'll tell you Vineet, the excitement, the satisfaction I got from a customer writing a check for $100,000 for that product was so much better than the 350 papers that I wrote in the past. I said, wow, this is awesome, right?
So that is what I learned, right? And unless you do it, you will never understand what it takes. And there were so many challenges on the way where we thought this company is going to fail. But we managed to make it successful and we sold it to Xilinx many, many years ago.
[00:35:34] Speaker B: And having done it twice now, that's perfect.
One more topic that I want to touch upon, I think which will be probably a deeply personal to you, right? You and your wife endured first ever chair professorship at your alma mater, IIT Kharagpur.
That seems deeply personal. What drove that commitment and what do you hope it sparks for the next generation of engineers?
[00:35:57] Speaker C: So I will. I'll tell you something Vinit, that whatever I've accomplished in my life, right? I mean again, I think I have done some interesting things. I've been in academia, I've been in startups, et cetera.
I actually owe it all to the fantastic education I got from it. IIT Kharagur. I mean those four years, those professors I had were the best. Again, I got education in the US too.
But the IIT Kharagpur education transformed me. And I know you are from IIT Madras and many of us from the IITs feel the same way. So I felt in a small way I should give back to my alma mater.
So I established the first chair professor in my department.
And I am hoping, and again I try to motivate so many of my students that it is the education that transforms you. Right? Education teaches you how to learn, right? It is not, oh this I learned about this particular thing, this particular algorithm. That algorithm that you learned will no longer be relevant, but it is the ability to learn is what you have learned.
And IIT Kharagpur taught me that. And that was my way of giving back to the community.
[00:37:13] Speaker B: Perfect. One last question, Prith. You've lived many different careers. People usually are either just an academic or a serial entrepreneur or they spend their life in corporate. And you have done all three successfully. If someone comes and asks you for an advice, a young engineer comes and asks you for an advice. What would be that one advice that you'll give them?
[00:37:34] Speaker C: It's funny you asked. Actually I responded to that in my previous podcast, the young engineer that I'd say, I'd say do something that you are truly passionate about. I mean my father taught me the concept of true north. What is true north? True north is something that fundamentally think this is what I really believe in. And I will not do something that is unethical or whatever just to cut corners to get to the top. Right?
True. Note is something I say if I did something wrong that I would be ashamed to if my mom finds out or if it says Preet Banerjee did this and it comes up in New York Times, I would be ashamed if that came out.
Don't do that. Never cut corners. That's the first advice. The second thing is be passionate in whatever you do. Right?
Just don't do oh my so and so. My dad was an academia IVA professor. Oh my uncle was an entrepreneur. I'll be an entrepreneur. Oh my aunt was an engineer. I will be an engineer.
Do what is passionate to you because if you follow your passion, you will thrive in academia, startup or the corporate world. Now nobody can stop you.
Passion first. Rest of things will follow.
[00:38:54] Speaker B: Perfect Prith. It was a fascinating conversation and it was great to know your thoughts. Especially you know, how the chip design and the entire silicon to system engineering is being shaped under your innovative guidance. It was a pleasure talking to you and thanks a ton again for the time.
[00:39:12] Speaker C: Thank you very much Vineet. I really enjoyed it and I hopefully the audience will like it too. But it's an honor to be invited to your podcast. I've seen many of the other speakers.
Thank you for inviting me.
[00:39:23] Speaker B: Thanks Prith.
[00:39:30] Speaker A: Thanks for listening to INA Insights. Please visit INA for more podcasts, publications and events on developments shaping the industrial and industrial technology sector.
[00:39:43] Speaker B: IT.
