Embracing the AI Frontier: A Seasoned Silicon Engineering Leader’s Journey - Let's Talk Fundamentals

My journey into AI as a silicon engineering leader starts with understanding its vastness. In this second blog (previous blog), I break down AI into foundational components and explore how they fit into our industry.

Bridging Two Worlds

Reflecting on the evolution of silicon engineering, we've transitioned from manual designs to leveraging sophisticated tools like HDLs and UVM. Each leap required us to adapt, learn, and grow. Today, AI presents another such leap—a shift from predominantly data-driven verification methods to more advanced: data driven and scenario-based techniques, such as dynamic test generation for edge cases. To make this leap, we must first understand the building blocks of AI and ML.

How AI is Transforming Silicon Engineering?

Artificial Intelligence (AI) refers to technologies that enable machines to perform tasks typically requiring human cognition, such as learning, reasoning, and problem-solving. In silicon engineering, AI drives innovation in semiconductor design and verification, enhancing efficiency and effectiveness in chip development processes.

The first major building block of AI is Machine Learning (ML), which focuses on algorithms that allow computers to learn from data and make decisions without explicit programming for each task. In silicon engineering, ML is potentially used for optimizing design, identifying design flaws, and enhancing verification processes. By analyzing large datasets, ML significantly reduces the time and cost of traditional development cycles.

Deep Learning (DL), a specialized subset of ML, uses artificial neural networks with multiple layers to model complex patterns in data. In silicon engineering, DL helps automate defect classification, enhance Electronic Design Automation (EDA) tools, and address complex design challenges in chip design and validation. Its hierarchical learning capabilities make DL crucial for solving intricate problems in frontend design.

Generative AI is a significant advancement in DL and potentially used to create/explore new design architectures, optimize layouts, and generate synthetic data for verification. Generative Adversarial Networks (GANs), for example, can generate new design variations, while Transformer models, such as GPT, assist in automating documentation, generating code for design scripts, and supporting frontend design problem-solving.

Generative AI has potentially numerous applications in frontend design automation; proposing new chip designs, and assisting in RTL (Register Transfer Level) coding. In verification, it can help generate testbenches and create dynamic scenario driven verification infrastructure to improve efficiency in identifying design flaws before tape-out.

AI in silicon engineering is largely focused on narrow AI—AI designed for specific tasks like optimising chip design or improving verification. Though progress toward General AI (AGI), which would have human-like general intelligence, remains theoretical, ongoing research aims to revolutionize semiconductor design in the future.

Machine Learning Paradigms

There are three major learning techniques for ML

• Supervised Learning : Trains models on labeled data to predict specific outcomes.
• Unsupervised Learning : Identifies patterns in unlabeled data for insight generation.
• Reinforcement Learning : Optimizes processes through trial and reward.

Supervised Learning

In supervised learning within the field of silicon engineering, models are trained on labeled datasets where each input (such as simulation data, process parameters, or design features) is paired with a known output label (like pass/fail status, defect type, or performance metric). This allows the model to learn the relationship between inputs and outputs, enabling accurate predictions on new, unseen data.

For example: During RTL design and development phase, verification engineers run extensive simulations to test functionality of DUT under various conditions with different IP/SOC configuration and input constraints. Each simulation generates signal traces that represent the behaviour of the block. These traces are analysed and labeled as 'pass' if the DUT meets design specifications or 'fail' if it doesn't.

By training a supervised learning model on these labeled traces, engineers can predict the outcome of new simulations quickly. This accelerates the verification process by focusing computational resources on simulations likely to fail, thereby identifying design issues earlier in the development cycle.

Supervised learning can help verification engineers can make better data-driven decisions to ensure higher product quality. The ability to predict outcomes and classify data accurately eventually lead to higher productivity and cost savings.

Unsupervised Learning

Unsupervised learning deals with unlabeled data. The model tries to find inherent patterns or groupings without predefined labels. In the context of silicon engineering, unsupervised learning is invaluable due to the massive amounts of data generated during the design and verification phases of IP/SOC development. Labeling every piece of data is impractical and unsupervised methods help engineers extract meaningful insights from raw data.

Using the same example from the previous section (Supervised Learning) sometimes it is impractical to perform supervised learning during the early stages of bring up. It takes a lot of effort by the engineers outside their main job to support supervised learning.

Unsupervised learning allows the models to put landmarks and cluster (group) around similar errors. Each cluster might represent a specific type of error, such as timing violations, power integrity issues, or signal integrity problems. Engineers can then prioritize clusters affecting critical functionalities and identify common root causes more efficiently, accelerating the debugging process.

Unsupervised learning can help in initial triage of an overnite regression run and help identify which test should be debugged first.

Reinforcement Learning

Reinforcement learning (RL) is a technique where an agent learns to make decisions by interacting with an environment, receiving rewards for positive outcomes and penalties for negative ones. In the field of silicon engineering, RL can be utilized for optimising complex processes, where a sequence of decisions needs to be made to achieve an optimal design or operational goal.

For instance, during the physical design phase of an IP or SOC, the placement and routing of millions of transistors must be optimized for power, performance, and area (PPA). An RL agent can be trained to iteratively make decisions on how to place and route components to achieve the best possible trade-off between these factors. The agent receives feedback based on PPA metrics, guiding it towards an optimal layout. This approach can significantly reduce the time spent on iterative manual optimizations, especially in large-scale designs.

Another application of RL in silicon engineering is automated verification. Instead of manually writing constrained random test scenarios, an RL agent can learn to generate stimuli that maximise coverage, thereby improving the efficiency of verification efforts.

Reinforcement learning helps automate decision-making processes that are typically labor-intensive and involve complex trade-offs, ultimately leading to shorter design cycles and more efficient silicon products.

In summary, each of these machine learning paradigms has distinct advantages in the context of silicon engineering:

• Supervised Learning is used when labeled data is available, enabling models to learn direct relationships between inputs and outputs, which can enhance verification and testing processes. Use Case: classify RTL simulation results as pass or fail, allowing faster identification of failing test cases and improving overall verification efficiency.
• Unsupervised Learning is useful for extracting insights from large datasets without explicit labels, facilitating error categorization and efficient debugging. Use Case: Applied to group similar waveform patterns from RTL simulations, helping engineers identify common failure modes and prioritize debugging efforts.
• Reinforcement Learning helps optimize complex processes and automate decision-making, particularly in physical design and verification. Use Case: Optimise testbench configurations, learning which configurations are more likely to expose design bugs and thereby improving functional coverage.

Why Verification Engineers Should Care

Verification engineering has always been about achieving certainty and predictability. But as designs grow increasingly complex, traditional methods struggle to keep up. AI and ML offer new ways to handle large datasets, recognise intricate patterns, and predict issues that traditional methods might miss.

For example:

• Efficient Debugging: Imagine trying to manually sift through millions of lines of simulation data to find a bug—a task that could take days or even weeks. ML models can analyse this data in minutes, pinpointing anomalies that would otherwise be buried in noise. One engineer reported saving over 50% of debugging time by using an ML-based approach.
• Predictive Analysis: AI can forecast potential design failures before they happen. For instance, if a certain combination of inputs has historically led to timing issues, AI can flag similar scenarios early in the verification process, allowing engineers to address them before they become costly problems.
• Automation of Routine Tasks: Verification involves many repetitive tasks, such as regression testing and running sanity checks. AI can automate these, allowing engineers to focus on more complex, creative problem-solving. One team used AI to automate routine verification tasks, such as identifying testlists and regression suites to run on a daily basis, freeing up more time for innovation.

Embracing the Probabilistic Nature

Transitioning from a deterministic mindset to embracing probabilistic models is a significant paradigm shift. In traditional verification, we typically deal with binary outcomes: a test either passes or fails, indicating a clear result. However, AI introduces probabilities and confidence levels, which may initially seem less definitive but provide a deeper, more nuanced understanding of system behaviors.

Consider a scenario where you're verifying the functionality of a processor. In deterministic verification, a directed test case would be created to check whether a specific instruction executes correctly. The outcome is straightforward: either the instruction works as expected (pass), or it doesn't (fail).

In contrast, with probabilistic models, you might use AI to assess how likely it is that the processor will correctly handle a range of similar instructions under different conditions (Timing / Latency / Memory Fills / Etc). Instead of just a binary pass/fail, the AI model might indicate, for instance, a 95% confidence that the processor will perform correctly across these scenarios. This probabilistic approach helps identify subtle edge cases or trends that a deterministic method might miss.

Think of it as expanding our toolkit. Just as we adopted HDLs and UVM to manage growing complexities, AI offers new methods to tackle the challenges of modern verification, providing insights that go beyond the black-and-white outcomes of traditional methods.

Getting Started with AI and ML

As we stand at the forefront of integrating AI into verification, here are some steps to begin this journey:

• Educate Yourself: Leverage online resources, courses, and workshops focused on AI and ML fundamentals. Use free online courses like Andrew Ng's 'Machine Learning' on deeplearning.ai or introductory books like 'Hands-On Machine Learning with Scikit-Learn and TensorFlow.'
• Hands-On Practice: Experiment with datasets related to verification. Try building simple models to solve specific problems. Open source tools such and Verilator and CocoTb are brilliant in building datasets (or synthetic data) that help in model training or build custom models.
• Collaborate and Share: Engage with the community. Share insights, challenges, and successes with peers.
• Stay Curious: The field of AI is vast and rapidly evolving. Continuous learning is key.

The fundamentals of AI and ML open up a world of possibilities for verification engineers. By understanding these concepts, we position ourselves to lead the next wave of innovation in silicon engineering.

Join the Conversation

What specific verification challenges do you face that AI could help solve? Have you tried incorporating ML models in your verification flow? Share your experiences!

Stay tuned for more insights as we continue this journey into the fusion of AI and verification engineering.

Appendix:

Key Terminologies for Verification Engineers

• Algorithm: A set of rules or calculations used to solve problems. In ML, algorithms process data to create models.
• Model: The mathematical representation of a real-world process. It’s what you get after training an algorithm with data.
• Training: The process of feeding data to an ML algorithm to help it learn.
• Dataset: The collection of data used for training and testing the model.
• Features: Individual measurable properties or characteristics used as input to the model.
• Labels: The output or target variable that the model is trying to predict (used in supervised learning).
• Overfitting: When a model learns the training data too well, including noise and outliers, and performs poorly on new, unseen data.
• Underfitting: When a model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and new data.

The Machine Learning Workflow

Understanding the ML workflow helps in visualizing how these concepts come together:

1. Problem Definition: Clearly define the problem you aim to solve. For verification, this could be predicting potential failure points in a design.
2. Data Collection: Gather relevant data. This might include simulation logs, test results, or performance metrics.
3. Data Preprocessing: Clean and format the data. Handle missing values, normalize scales, and encode categorical variables if necessary.
4. Feature Engineering: Select and construct meaningful features that influence the output.
5. Model Selection: Choose an appropriate algorithm based on the problem type (classification, regression, clustering).
6. Training: Use the training dataset to allow the model to learn the patterns.
7. Evaluation: Assess the model's performance using metrics like accuracy, precision, recall, or F1 score.
8. Hyperparameter Tuning: Adjust the model’s parameters to optimize performance.
9. Deployment: Integrate the model into the verification workflow for real-time predictions or analysis.
10. Monitoring and Maintenance: Continuously monitor the model’s performance and update it with new data as needed.