Hello everyone. Today I'll be discussing how machine learning is transforming the landscape of high speed protocol testing. We'll explore innovations in both hardware and verification techniques and how AI is driving new levels of efficiency and accuracy in these critical areas. Okay, so let's take a closer look at the key challenges we face when verifying modern high speed protocols. As interface speeds increase in standards evolve, our traditional methods of verification are simply not keeping pace. First time is a major constraint. Verification timelines are shrinking while the complexity of protocols is growing exponentially. Traditional methodologies re rely heavily on manual test creation and execution, which can't scale fast enough to meet today's aggressive development cycles. This results in longer debug cycles and a high risk of missing critical issues before tape out or release. Next, we have coverage gaps despite having large test suites. Conventional approaches often miss rare edge cases, especially at protocol boundaries or during state transitions. These are precisely the areas where critical failures occur in the field, and catching them late in the cycle can be extremely costly. The inability to guarantee full state space coverage leaves a blind spot in overall system quality. Lastly, scaling is a growing problem. Emerging standards like PCI Gen five, gen six, USB four come with exponentially more states configurations and timing requirements than their predecessors. Verifying, verifying them thoroughly requires orders of magnitude. More test vectors and existing tools or methods can't build to handle that kind of scale efficiently, to effectively tackle the challenges of protocol verification. We have developed a Hy Hybrid approach that combines the strengths of formal methods, dynamic simulation, and AI integration. This layered strategy allows us to maximize coverage, accuracy, and efficiency in validating high speed protocols. First, we use formal verification to mathematically prove the correctness of the protocol behavior. These techniques such as symbolic execution and theorem proving are exhaustive, meaning they can explore all possible states and transitions within a protocol. All a key strength here is for zero false negatives. If the formal method. If the formal model passes, we are guaranteed that certain classes of bugs simply don't exist. This is especially critical for ensuring absolute compliance with protocol specs at the architectural level. Formal methods alone don't capture real world hardware behavior, which is where dynamic simulation comes in. Simulation continue. Simulation provides hardware accurate testing, letting us observe how the implementation behaves under actual traffic, variable timing conditions, and diverse system configurations. It's essential for catching edge cases that depend on electrical noise, race conditions, or timing violations. Scenarios, formal methods may ab, ab abstract anyway. So the third layer of our approach is AI integration, which acts as a smart bridge between formal and simulation environments. Machine learning algorithms analyze verification results and dynamically adjust test priorities. By using coverage data and prob problematic fault models, AI helps us focus on the areas most likely to contain bugs significantly improving verification efficiency. This creates a feedback loop where insights from simulation inform formal analysis and vice versa, continually refining the test process. By combining the precision of formal methods, the realism of simulation, and the adaptability of ai, our hardware approach delivers a comprehensive, scalable solution for high speed protocol verification. This synergy allows us to achieve faster time to coverage and higher confidence in protocol compliance. Now let's look at the performance impact of integrating FPGA acceleration into our verification framework. These results de demonstrate how hardware acceleration can dramatically improve the speed and scalability of protocol testing. First, we achieved. 85% reduction in overall verification time compared to traditional CPU based methods. This is a game changer, especially for teams working under tight time to market constraints. FP Gs allow us to offload computationally intensive tasks such as. Packet parsing protocol, state monitoring and real time error detection directly into hardware. This result is significantly faster, results in significantly faster test execution without compromising accuracy. Next, we have reached two. Operation Latencies, which is a mega major breakthrough. This ultra low latency enables real time protocol analysis where the system can react almost instantly to protocol events or anomalies. It's particularly valuable for debugging time sensitive issues and observing transient faults that might be missed in slower environments. Finally, our 12 x increase in trans transaction throughput is due to the inherent parallelism in FPGA architectures. Unlike CPUs, which process sequentially, FPGS can execute multiple verification tasks simultaneously, making them ideal for handling high speed, high volume data streams. This allows us to ra. Run far more test vectors in less time, covering a broader spectrum of protocol behavior. These results clearly show that FPGA acceleration isn't just an optimization, it's an enabler for next generation verification demands. With faster execution, real time responsiveness, and massive increased throughput. FPGAs unlock new possibilities for scalable high performance protocol testing. So at the heart of our verification framework is the AI driven verification core, a powerful. Engine that transforms how we approach protocol validation. It uses machine learning, not just for automation, but for intelligent decision making throughout the verification process. The core begins with decision intelligence, where problem prob problem models. Problem models I use to predict high risk, edge cases up to 94% of accuracy. This allows us to eliminate redundant test cases, focusing our efforts where they matter most. Instead of brute force testing, we apply smart targeting, save both time and computational resources. Using deep learning algorithms, the system continuously analyzes historical test data to uncover su subtle protocol violations that might escape traditional checks. These could be timing anomalies, rare sequence violations, or usage patterns that deviate from spec but aren't immediately obvious. With this intelligence, we can dynamically. Prior prioritize test scenarios based on likelihood of exposing hidden bugs. The core continuously refines its strategy, adapting to feedback from ongoing simulations and formal analysis. Finally, AI enables semantic coverage analysis, mapping each aspect of the protocol spec to actual test scenarios or test cases. This ensures a hundred percent functional coverage, not just in terms of code or state space, but in how the system aligns with real world protocol, behavior, and intent. It also helps identify coverage blind spots early, reducing the risk of missed issues in production. In summary, our AI driven verification core delivers faster. Smarter and more thorough testing by com. Combining predictive analysis, learning from experience, and actively guiding the verification process. It's a major step forward from static rule-based verification toward a truly adaptive, intelligent led methodology. So this slide outlines how we've implemented supervised learning to enhance our verification workflow by learning from historical verification out outcomes we have built. Models that predict and prioritize the most impactful test ca test, test vectors or test cases. Again streamlining the entire process. So we first start with a large data set compiled from historical verification results. This includes both passing and failing cases. What sets our approach apart? Is the expert driven classification of past defects, which provides meaningful context and labels for the learning process. This label data gives the model a deep understanding of what constitutes a meaningful protocol violation and what patterns typically lead to them. For each protocol, we use custom neural network architectures tuned to unique. Characteristics of that network, such as timing parameters or timing patterns like you call it, sequence behavior or encoding anomalies. We also apply advanced hyper parameter tuning to optimize for both prediction accuracy and model generalization, ensuring that the model isn't just memorizing past failures, but truly learning how to anticipate new ones. Once trained, these models are deployed via lightweight inference engines within the en, within the verification environment. They identify high yield test vectors, those most likely to uncover bugs with an impressive 97% precision. And because this is a self-improving system, it continuously. Updates and refines its predictions with every new round of verification data. This creates a closed learning loop that gets smarter and more efficient over time. In summary, our supervised learning implementation transforms historical verification knowledge into real time predictive power. It's a strategic upgrade from reactive testing to proactive, intelligent verification, ensuring we spend time where it matters most. I. So one of the most time consuming aspects of protocol verification is generating effective and comprehensive test cases. A solution uses machine learning powered test generation, which significantly out. Outperforms traditional, manual or rule-based methods. Traditionally, test generation relies on expert written scenarios or constrained random techniques. These methods can be slow, redundant, and often mis edge cases, especially in high complex protocols with massive state spaces. Our system uses machine learning algorithms to learn from prior test results and protocol specifications. It automatically generates test cases that are both relevant and high impact, focusing on areas likely to reveal hidden bugs or unverified behavior. What sets our approach apart is that it excels in both volume and precision. We are able to generate far more test vectors in a short time while maintaining a high signal to noise ratio, meaning fewer redundant or low value tests. This leads to more efficient simulations, faster coverage closure, and a higher likelihood of uncovering critical bugs early. The system also adapts as as verification progresses dynamically adjusting its output based on gaps in coverage and feedback from failed tests. It's a li, it's a living learning test generator that evolves with the design and verification process itself. In summary, our automated test generation system delivers smarter, faster, and more complex, a more complete coverage f freeing engineering teams from repetitive test development and allowing them to focus on deeper analysis and debugging. So this this slide breaks down the custom neural network architecture we've designed for protocol verification. It's built from a ground up to handle the unique demands of high speed interface testing, combining deep learning techniques with protocol specific intelligence. So the first part is the input layer. The input layer is ca carefully crafted to capture a rich and relevant feature set. It includes protocol specific PA parameters, such as encoding schemes, error checking rules, and RU and flow control. We also feed in precise timing and synchronization data, essential for identifying timing related violations. Another key input is the vector of state transitions. These represent the full behavior model of the. Of the protocol, allowing the network to understand expected sequences. Finally, we aggregate historical defect patterns, giving the model context from past issues and their signatures. Next we look at the hidden layers. In the hidden layers. We leverage a mix of deep learning techniques. So we look at T cns, which is the temporal conation networks that are used for pattern recognition across time series data, which is typical in waveform and transaction streams. Bidirectional LLS TMS allow the model to understand protocol behavior in both forward and backwards sequence flows critical for protocols with. Complex handshake or error recovery. Multi-head attention. MEChA mechanisms help the model focus on specific parts of the sequence, such as state transitions, where violations are most likely to occur. We also use fully connected layers in high dimensions to allow the allow for complex feature interactions and decision making at a semantic level. So lastly, the output layer. This layer supports multi key verification tasks, such as prioritizing test vector generation with each test ranked by a confidence scoreboard on predicted. Impact and risk predictive coverage mapping which highlights which parts of the protocol spec are insufficiently tested or missing altogether, real time violation detection, where the model can flag anomalies as they occur during simulation. And a statistical probability distribution for likely defect types of protocol failure categories, enabling smarter debugging and reporting. This architecture isn't just about prediction, it's about driving. Actionable intelligence into the verification process. It helps us test smarter, identify issues earlier, and ultimately ensure high protocol reliability and compliance. Yeah, this is just a slide which talks about specific results of different protocols like PCIE five, USP four, ddr, R five H, DM I 2.1. The different protocols. These are all the high speed. Protocols that have, that are being used in these days. And, how we have been able to reduce the time that is needed for like generating, or basically testing, like generating, as well as testing out all these different cases as we have, as I have explained in the previous slides it also gives you a chart of how coverage, where, how much of coverage we get out of our our the. The neural network that I was talking about, the algorithms that we've put in place, so how it actually helps with coverage and also bug detection. With this, instead of going in for east spins are generally so expensive, this is very good for bug detection because you keep running, you keep learning. The system keeps learning and keeps running those tests, which have which have sourced bugs in the past. So it's a good repetitive process of finding out where the bugs really recite and regressing the system further. So in this slide, we focus on how feature selection is handled by our AI driven verification system. Feature selection is critical for ensuring we focus on the most impactful protocol parameters, enabling more efficient and effective testing. First, we use advanced machine learning algorithms to analyze the protocol specifications. The goal is to automatically identify and extract the most critical parameters that need to be verified. These could include packet types, state transitions, timing sequences, or error conditions. By intelligently focusing on these key parameters, we reduce the amount of irrelevant data being processed, ensuring a more targeted verification approach. The system then moves to correl correlation detection, where it uncovers complex relationships between verification variables. For example, it might find that certain protocol violations only happen under specific timing conditions, or when specific combinations of inputs occur. Recognizing these interdependencies allows the system to generate tests that explore edge cases, scenarios that are critical, but often missed in conventional testing. Once correlations are detected, the system applies feature ranking to prioritize verification parameters. Each parameter is ranked based on its impact on overall protocol coverage and its potential to uncover defects. This ensures that the most critical parameters are tested first, improving the efficiency and effectiveness of the verification process. Finally, the system is self-optimizing. It continually refines its feature selection strategy as verification progresses. With each test, itration, new insights are incorporated, allowing the system to adapt and evolve its approach based on real. Real time findings. This dynamic refinement allows, ensures that the test coverage always remains comprehensive and test resources are directed where they're needed most. In summary, feature selection intelligence enhances our verification process by ensuring that we focus on the most critical parameters and edge cases. This intelligent data driven approach leads to smarter testing, better coverage, and ultimately more reliable protocol validation. Okay. Lastly this is in conclusion as we look to the future, the role of AI and machine learning in hardware verification will only continue to grow, making the process smarter, faster, and more adaptable. Let's explore three key trends shaping the next generation of hardware verification. First, we are moving towards enhanced ML integration, especially with the use of reinforcement learning. Reinforcement learning will allow our verification systems to self optimize over time learning. Self optimize over time learning the most efficient test strategies based on feedback from each test cycle. In addition, predictive models will advance to a point where they can anticipate potential design flaws before a protocol can even be implemented, reducing costly post design fixes. Essentially this means we'll catch issues earlier, saving both time and resources, while improving design reliability. Another exciting development is cross protocol intelligence as different protocol types evolve. Like ethernet P-C-I-E-U-S-P, we will see knowledge transfer between domains. This means insights gained from. Verifying one protocol can be applied to another, accelerating the entire verification process Over time we'll start seeing universal verification patterns emerge. Simplifying the testing process across diverse interface types and making it easier to validate new protocols. Finally, we are heading toward a verification driven design paradigm. AI Insights will no longer be a afterthought. Tale. Actively guide hardware design from the outset by shifting verification left. In other words, moving it earlier in the development timeline, designers will have instant feedback on the robustness and correctness of their designs. This will not only help to identify issues earlier. But also drive more reliable, optimized hardware from the very beginning of the design process. So in conclusion, the hardware. The future of the hardware verification is deeply intertwined with AI and ml enhanced ML integration. Cross-functional intelligence and a verification driven design approach will re revolutionize how we approach hardware validation leading to faster, smarter, and more reliable designs. Thank you for your time.