Cloud native EDA tools & pre-optimized hardware platforms
Driven by the ever-growing demand for computing power, autonomous systems, and next-generation architectures, the semiconductor industry is experiencing a never-before-seen shift toward pervasive intelligence. As a result, silicon complexity and the software content in systems and products continue to increase at an exponential rate.
Look at the automotive industry, for instance. Manufacturers and their suppliers are re-architecting vehicles and system-level components with new and hotly debated electrical/electronic (EE) architectures that help create differentiation. This trend extends to other markets such as industrial automation, high-performance computing (HPC) data centers, and aerospace, with software-defined systems driving massive computing power, both at the edge — in the latest smartphones, cars, and cameras — and within core data centers where the data converges.
With AI now influencing almost every aspect of modern technology, chip designers are presented with new challenges to ensure their products meet performance expectations and operate reliably and efficiently under relentless computational demands. These changes are pushing the limits of traditional design and verification methodologies, creating a need for innovative solutions to address modern chip and system architectures.
Enter: Hardware-assisted verification (HAV), an essential approach that helps engineers improve design behavior to manage complexity and ensure systems function seamlessly in real-world applications.
In this blog post we’ll explore the role of HAV, examining different use cases, AI’s role in shaping new design requirements, and the need for flexible industry solutions to develop sophisticated chips.
To understand the complexities of modern chip development, it’s important to take a step back and examine each stage of the process, starting with the foundational building blocks of silicon IP. These blocks serve as the core of advanced chip architecture. What begins as silicon IP evolves into complex systems that, when paired with software, create applications designed to improve our daily lives. To achieve optimal performance, every component from silicon IP to system hardware and software must be co-optimized.
Developers have two primary options. They can either build these blocks in-house, leveraging their own expertise and proprietary designs, or they can license them from industry leaders such as 草榴社区. But the journey doesn't end with IP selection, integration, and chip creation. Silicon chips are subsequently integrated into boards, forming the backbone of powerful accelerator systems. These systems are then designed to drive and execute a vast array of software, from low-level drivers to high-level AI frameworks and applications. This holistic strategy ensures that each element works harmoniously, maximizing efficiency and performance across the entire AI acceleration ecosystem.
While this progression from IP blocks to fully-fledged AI accelerator systems may seem straightforward, the reality is far more nuanced. The key question is: What needs to be done to ensure these systems function as intended at every stage of the process and from different perspectives like functional correctness, performance, and power?
Ensuring everything from IP to system and software runs correctly is no small feat. As depicted in the chart below, several critical use cases must be thoroughly validated throughout the design process.
Functional verification through RTL regressions, IP performance validation, and compliance checks are essential to confirm that the IP behaves as expected in a system-level environment. Early-stage performance analysis, low power assessments, and test generation are equally crucial to ensuring efficiency and reliability. Additionally, key considerations such as safety, security, and silicon lifecycle management must be incorporated to meet modern design demands.
As products evolve and the complexity of workloads and systems increase, traditional verification techniques become less viable and new methodologies must be utilized.
HAV — emulation and prototyping that allow for much faster execution of verification tasks — was introduced not long ago to augment simulation-based verification and other traditional techniques, but only for the most advanced designs. With chip and software complexity increasing, pre-silicon design cycles lengthening, and thorough hardware-software validation becoming more critical, HAV is now mandatory for most designs today.
Engineers must now validate not just individual components, but complex system-wide interactions between hardware and software. This includes ensuring low-level IP compliance and optimizing performance and power, making validation a monumental task.
Integrated verification engines — from simulation-based tools at various abstraction levels to hardware-assisted solutions like emulation and FPGA-based prototyping — enable efficient execution of extensive workloads, addressing crucial scalability needs. Cloud-based solutions are particularly valuable in this context to further optimize performance and cost-efficiency, allowing simultaneous processing of numerous workloads and regressions through a hybrid approach utilizing both on-premises and cloud-based resources.
The key to effective verification lies in seamlessly transitioning between different use cases, from rapid early-stage RTL debugging to high-performance software workload validation. HAV platforms that are configurable and efficiently address this spectrum are essential to validate real-world chip and system design complexities that require real-world workloads. With the insatiable demand for verification cycles, design teams must also be conscious to achieve the best ROI for each of the verification engines. While there is no engine that fits all use cases, we have entered an age in which re-configurability of HAV setups across projects or even within projects becomes a critical component of ROI considerations, joining the classic concerns of direct acquisition costs and indirect costs like power consumption of verification setups.
Ultimately, an adaptable verification architecture is crucial, accommodating diverse user requirements and use cases while remaining agile enough to meet future technological demands in this ever-evolving landscape.
As a leader in advanced chip design tools and silicon IP, 草榴社区 stands at the forefront of this evolution. From enabling first silicon success to the development of complex silicon architectures that form the backbone of modern devices, we recognize that HAV is a necessity for long-term innovation.
It delivers the tools and infrastructure required to validate intricate hardware-software interactions. It improves system and product quality and significantly accelerates time-to-market. And it reduces costly silicon respins to unlock the next technological frontier. In doing so, HAV is transforming the design process and empowering engineers to realize their most ambitious visions with confidence and efficiency.