If keeping the beat is the most important thing in the game, then you build the game around keeping the beat. There are many different ways to approach the problem, but if I were building such a system myself, I would start with a system to handle a data-driven beat (e.g. this level/song sets the beat to X, that level/song sets the beat to Y) and then build all of my visuals and gameplay on top of that. The key component to making this work would likely be an animation system that could scale animations faster or slower in order to match the timing of the music.

On the data side, this would mean all animations would be built so they could be sped up by dropping frames, or slowed down by holding certain frames for additional length. All animations would also need to be the same length (or a multiple of a standard length), so that I can ensure the animations will fit into a standard musical measure. If I wanted to have a variety of attack animations and hit reactions, I would probably also establish a set of rules that each attacking and hit reaction animation must always be the same number of frames. I could further standardize each attack and hit reaction active frame happening on the same frame each time.

The code side would then play my animations to those musical measures along the beat. It would do so by scaling the animations longer or shorter based on the beat. The system could add or cut animation frames so that each animation can play in sync with the music. Once I've got the animation system integrated with the beat-keeping system, I can then ensure each animation should start playing on the appropriate frame to keep the beat. As long as the animations are scaled to the musical measure and the music keeps the same beat for its entire duration, the animations should always sync to the beat of the music.

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In the age where data reigns supreme, the semiconductor industry stands on the cusp of revolutionary change, redefining complexity and productivity through a lens crafted by artificial intelligence (AI). The intersection of AI and the semiconductor industry is not merely an emerging trend—it is the fulcrum upon which the next generation of technological innovation balances. Semiconductor companies are facing a critical juncture where the burgeoning complexity of chip designs is outpacing the growth of skilled human resources. This is where the infusion of AI-powered data analytics catalyzes a seismic shift in the industry’s approach to efficiency and productivity.

AI in semiconductor design: A revolution beckons

With technological leaps like 5G, AI, and autonomous vehicles driving chip demand, the status quo for semiconductor design is no longer sustainable. Traditional design methodologies fall short in addressing the challenges presented by these new technologies, and the need for a new approach is non-negotiable. AI, with its capacity to process massive datasets and learn from patterns, offers a revolutionary solution. Gathered with vast amounts of electronic design automation (EDA) data, machine learning algorithms can pave an efficient path through design complexities.

Navigating the complexity of EDA data with AI

The core of semiconductor design lies in the complexity of EDA data, which is often disparate, unstructured, and immensely intricate, existing in various formats ranging from simple text to sophisticated binary machine-readable data. AI presents a beacon of hope in taming this beast by enabling the industry to store, process, and analyze data with unprecedented efficiency.

AI-enabled data analytics offer a path through the labyrinth of EDA complexity, providing a scalable and sophisticated data storage and processing solution. By harnessing AI’s capabilities, the semiconductor industry can dissect, organize, and distill data into actionable insights, elevating the efficacy of chip design processes.

Leveraging AI in design excellence

Informed decisions are the cornerstone of successful chip design, and the fusion of AI-driven analytics with semiconductor engineering marks a watershed moment in the industry. AI’s ability to comprehend and process unstructured data at scale enables a deeper understanding of design challenges, yielding solutions that optimize SoCs’ power, performance, and area (PPA).

AI models, fed by fragmented data points from EDA compilation, can predict bottlenecks, performance constraints, or power inefficiencies before they impede the design process. This foresight empowers engineers with informed design decisions, fostering an efficient and anticipatory design culture.

Reimagining engineering team efficiency

One of the most significant roadblocks in the semiconductor industry has been aligning designer resources with the exponential growth of chip demand. As designs become complex, they evolve into multifaceted systems on chips (SoCs) housing myriad hierarchical blocks that accumulate vast amounts of data throughout the iterative development cycle. When harnessed effectively, this data possesses untapped potential to elevate the efficiency of engineering teams.

Consolidating data review into a systematic, knowledge-driven process paves the way for accelerated design closure and seamless knowledge transfer between projects. This refined approach can significantly enhance the productivity of engineering teams, a crucial factor if the semiconductor industry is to meet the burgeoning chip demand without exponentially expanding design teams.

Ensuring a systemic AI integration

For the full potential of AI to be realized, a systemic integration across the semiconductor ecosystem is paramount. This integration spans the collection and storage of data and the development of AI models attuned to the industry’s specific needs. Robust AI infrastructure, equipped to handle the diverse data formats, is the cornerstone of this integration. AI models must complement it and be fine-tuned to the peculiarities of semiconductor design, ensuring that the insights they produce are accurate and actionable.

Cultivating AI competencies within engineering teams

As AI plays a central role in the semiconductor industry, it highlights the need for AI competencies within engineering teams. Upskilling the workforce to leverage AI tools and platforms is a critical step toward a harmonized AI ecosystem. This journey toward proficiency entails familiarization with AI concepts and a collaborative approach that blends domain expertise with AI acumen. Engineering teams adept at harnessing AI can unlock its full potential and become pioneers of innovation in the semiconductor landscape.

Intelligent system design

At Cadence, the conception of technological ecosystems is encapsulated within a framework of three concentric circles—a model neatly epitomized by the sophistication of an electric vehicle. The first circle represents the data used by the car; the second circle represents the physical car, including the mechanical, electrical, hardware, and software components. The third circle represents the silicon that powers the entire system.

The Cadence.AI Platform operates at the vanguard of pervasive intelligence, harnessing data and AI-driven analytics to propel system and silicon design to unprecedented levels of excellence. By deploying Cadence.AI, we converge our computational software innovations, from Cadence’s Verisium AI-Driven Verification Platform to the Cadence Cerebrus Intelligent Chip Explorer’s AI-driven implementation.

The AI-driven future of semiconductor innovation

The implications are far-reaching as the semiconductor industry charts its course into an AI-driven era. AI promises to redefine design efficiency, expedite time to market, and pioneer new frontiers in chip innovation. The path forward demands a concerted effort to integrate AI seamlessly into the semiconductor fabric, cultivating an ecosystem primed for the challenges and opportunities ahead.

Semiconductor firms that champion AI adoption will set the standard for the industry’s evolution, carving a niche for themselves as pioneers of a new chip design and production paradigm. The future of semiconductor innovation is undoubtedly AI, and the time to embrace this transformative force is now.

Cadence is already at the forefront of this AI-led revolution. Our Cadence.AI Platform is a testament to AI’s power in redefining systems and silicon design. By enabling the concurrent creation of multiple designs, optimizing team productivity, and pioneering leaner design approaches, Cadence.AI illustrates the true potential of AI in semiconductor innovation.

The harmonized suite of our AI tools equips our customers with the ability to employ AI-driven optimization and debugging, facilitating the concurrent creation of multiple designs while optimizing the productivity of engineering teams. It empowers a leaner workforce to achieve more, elevating their capability to generate a spectrum of designs in parallel with unmatched efficiency and precision, resulting in a new frontier in design excellence, where AI acts as a co-pilot to the engineering team, steering the way to unparalleled chip performance. Learn more about the power of AI to forge intelligent designs.

The post AI-Powered Data Analytics To Revolutionize The Semiconductor Industry appeared first on Semiconductor Engineering.

The purpose of the verification plan, or vplan as we call it, is to capture all the verification goals needed to prove that the device works as specified. It’s a big responsibility! Getting it right means having a good blueprint for verification closure. However, getting it wrong could result in bug escapes, wasting of resources, and possibly lead to a device failing altogether. With the focus on AI-driven verification, the efficiency and effectiveness of verification planning are expected to improve significantly.

There are several key elements needed to create a good vPlan. We will go over a few below.

Accurate verification features are needed for verification closure

The concept of divide and conquer suggests that every complex feature can be broken down into sub-features, which in turn can be further divided. Verisium Manager’s Planning Center facilitates this process by enabling users to create expandable/collapsible feature sections, a crucial capability for maintaining quality. Not having this key capability can put quality at risk.

Close alignment to the functional specification

Close adherence to the functional specification is crucially linked to the first point. Any new features or changes to existing ones should prompt immediate updates to the vPlan, as failing to do so could affect verification quality. The Planning Center allows users to associate paragraphs in the specification to the vPlan and provides notifications of any corresponding alterations. This allows users to respond by adjusting the vPlan accordingly in alignment with the specifications.

Connecting relevant metrics, vPlan features

Once the vPlan is defined, it’s important to connect the relevant metrics to demonstrate verification assurance of each feature. It may involve using a combination of code coverage, functional coverage, or directed test to provide that assurance. The Planning Center makes connecting these metrics to the vPlan very straightforward. Failing to link these metrics with the features could result in insufficiently verified features.

Showing real-time results

To effectively monitor progress and respond promptly to areas requiring attention, the vPlan should dynamically reflect the results in real time. This allows for accurate measurement of progress and focused allocation of resources. Delayed results could lead to wasted project time in non-priority areas. Verisium Manager’s vPlan Analysis automates this process enabling users to view real-time vPlan status for relevant regressions.

Customers have shared that vPlan quality significantly influences project outcomes. It’s crucial to prioritize creating higher quality vPlans, rather than simply focusing on speed. However, maintaining consistent high quality can be challenging due to the human tendency to quickly lose interest, with initial strong efforts tapering off as the process continues.

A thorough verification plan is the key to success in ASIC verification. Verification reuse is critical to the productivity and efficiency of system-on-chip (SoC), and a good vPlan is the first step in this direction. If you’re a verification engineer, take the time to develop a thorough verification plan for your next project. It will be one of the best investments you can make in the success of your project.

The post Weak Verification Plans Lead To Project Disarray appeared first on Semiconductor Engineering.