PoV – Evolution of EDA Tools to Meet the Rising Demands of High-Performance SoCs
The explosion in computational complexity, networking speeds, and Artificial Intelligence is fundamentally reshaping the semiconductor industry as well. These advancements are driving the need for SoCs that pack massive volumes of logic and HDL code, resulting in billions of transistors being integrated onto a single silicon die.
Thanks to relentless scaling in technology nodes – from 10nm to 5nm, 3nm, and now 2nm and below – it is now possible to accommodate these enormous transistor counts within compact die areas, without drastically increasing chip size. However, this miniaturization comes at a cost. As SoCs grow in capability and density, physical design becomes exponentially more complex. Traditional design approaches and tools are no longer self-sufficient to manage the scale, speed, and power requirements of modern design, especially under today’s tighter time-to-market constraints.
Why this blog?
With each new generation, designing SoCs is becoming complex, and this rapid progression places immense pressure on the EDA tools, which are being pushed to adapt at an equally rapid pace. This article provides a practical perspective on the state of modern chip design. It examines how engineering teams are rethinking their design methodologies, the growing role of AI/ML in design workflows, and its future with rising complexity.
Q1: What are the growing demands on ASIC Physical Design, and how are the Chipmakers keeping up?
Modern SoCs feature high-speed logic operating at a frequency of several GigaHertz (GHz), intricate clocking schemes, multiple voltage and power domains, and extremely tight area and power budgets. All of this must be managed while ensuring functional correctness, timing closure, and manufacturability—often within a few months instead of a year or more.
Physical Design Architects/Engineers are now expected to manage billions of placeable standard cells, Microscopic routing constraints, Multi-Mode, Multi-Corner (MMMC) analysis views, Power integrity, and Electro-Migration (EM) risks, as well as Thermal-Aware Floor planning.
One of the most significant enablers for managing these challenges has been the continuous evolution of ASIC/VLSI EDA tools.
Q2: How have EDA tools and methodologies evolved to meet the modern SoC needs?
In the early days of SoC design, EDA Tools had limited functionality, and design complexity was modest. No single EDA vendor could offer a complete solution for the full Physical Design flow, leading teams to adopt best-in-class tools from different vendors for various stages, such as Synthesis, Place-and-Route(P&R), and different Sign-Off checks.
This approach worked well initially, but as design complexity grew, correlation mismatches between Logic Synthesis and Place & Route (P&R) became problematic. To address correlation issues between stages, ASIC EDA Tool Vendors began developing more comprehensive, tightly integrated tool chains. Using tools from the same Vendor for Logic Synthesis and Place-and-Route(P&R) improved timing correlation as we move forward in the ASIC development flow.
As Complexity increased further, design and development teams began to selectively mix EDA tools across Vendors again – leveraging the unique strengths of each – to optimize critical phases within P&R itself and achieve better PPA and design closure. While this introduced more integration (including import and export of design into different EDA Tools), overhead and license cost, and license management challenges, it offered greater flexibility and performance benefits.
The latest leap in EDA tool evolution is the integration of Artificial Intelligence and Machine Learning (AI/ML) into design workflows, including ASIC Physical Design. AI-assisted EDA tools are fundamentally transforming physical implementation by enabling Intelligent floor planning and cell placement based on automated learning with iterations on the fly, Predictive timing and congestion analysis, Automated design space exploration, and Real-time PPA tradeoff optimization.
Examples include Synopsys “Design Space Optimization using AI” and Cadence’s “Cerebrus Intelligent Chip Explorer,” both of which are being used in production today to reduce iteration cycles, accelerate convergence, and outperform traditional human-guided flows. Siemens Aprisa is actively investigating machine learning–driven design flows. While NVIDIA’s AutoDMP and DREAMPlace apply deep learning, graph neural networks, and reinforcement learning to enhance placement, floorplanning, and timing closure. These innovations are helping define the future of AI-powered EDA solutions.
Q3: How have design methodologies evolved in parallel with EDA tools to address SoC complexity?
Alongside tool innovation, Physical Design Methodologies have also evolved to meet rising demands. Key shifts include Hierarchical and Top-Down design approaches to enable partitioning and parallel development.
The adoption of hybrid standard cells and mixed standard cell row heights enables more fine-grained optimization of power, performance, and area (PPA), which is especially important at advanced process technology nodes such as 3nm and below.
Physical-Aware Logic Synthesis incorporates Placement and Congestion information during Synthesis to improve Timing, Area, and Routability. Exploiting useful clock skews starting at Logic Synthesis enables enhanced optimization of critical timing paths by leveraging path-specific clock arrival variations. Early integration of timing, power, and reliability checks to avoid signoff bottlenecks. Pushing IR drop analysis, DRC, and thermal checks into early implementation phases. Concurrent Verification, including Formal Verification (at each stage) and Physical Checks (after floorplan or initial placement). Incremental and iterative flows that allow reuse of intermediate results for faster convergence.
Together, these methodologies improve scalability, design predictability, and reduce overall turnaround time in developing complex SoCs.
Q4: Why do AI-driven EDA tools matter for the future?
AI-enabled EDA tools are ushering in a new era of silicon design. They not only help manage complexity, but they also unlock new possibilities. With AI in the loop, the design and implementation of complex architectures—such as 3D ICs, chiplets, and heterogeneous SoCs – are likely to become more efficient and scalable, further accelerating their practical adoption. Design turnaround times shrink, accelerating innovation. Smaller and more agile design teams can compete at the highest levels, thanks to automation and built-in intelligence.
AI-driven tools represent a paradigm shift from manual optimization to data-driven automation, offering the speed, precision, and scalability needed for next-generation SoC Development.
To conclude, EDA tools have undergone a remarkable transformation – from basic logic synthesis and layout utilities to comprehensive, AI-powered platforms that drive the entire SoC development cycle, including Physical Design. As transistors count skyrockets, process nodes shrink, and market timelines tighten, the role of EDA tools has never been more critical.
The evolution from single-vendor Physical Design flows to flexible, intelligent, and adaptive design ecosystems is empowering chipmakers to meet rising demands and create the next wave of high-performance, low-power, and deeply integrated SoCs. The future of SoC design lies not just in managing complexity, but in mastering it through smart automation and collaborative design methodologies.
MosChip® combines extensive experience with strong industry partnerships to simplify and accelerate the design of high-performance SoCs. Their expertise spans throughout the entire process – from initial concept to final silicon – ensuring the chips are reliable and meet specifications the first time. If you need a trusted partner to turn your complex chip ideas into successful products, MosChip® is ready to assist you at every stage.
