Is it possible to develop high-performance EDA tools in Python?
With every generation, chips have become more complex and transistor counts have increased exponentially (according to the famous Moore’s Law). This exponential growth in complexity and size has led to a corresponding growth in EDA tool data-base sizes (HDL files, simulation logs, waveform dumps, net-lists, timing reports, GDSII etc) as well as compute power required to processes these data-bases. Most EDA tools are compute intensive as well as memory intensive; demanding high performance from a compute as well as capacity standpoint.
Given the very stringent performance requirements of EDA tools, is it a good idea to use Python as a mainstream development language for EDA tools? We will try to answer this question by sharing some experiences from a tool development project at Arrow Devices.
Related Semiconductor IP
- Narrow band - IoT Release-14 e-NodeB PHY. (L1) IP
- Narrow band - IoT Release-14 e-NodeB Protocol Stack (L2-L3) Software IP
- Narrow band - IoT Release-14 UE PHY. (L1) IP
- Narrow band - IoT Ultra-Low power UE RF Transceiver IP
- Narrow band - IoT Release-14 UE Protocol Stack (L2-L3) Software IP
Related Blogs
- What's driving 3D IC design? Do 2D EDA tools need a total overhaul to support 3D design?
- 10 ways to get your EDA tools to run faster, smoother, and longer
- ICCAD Keynote: Design of Secure Systems - Where are the EDA Tools?
- EDA AI Agents: Intelligent Automation in Semiconductor & PCB Design
Latest Blogs
- A Repeatable Framework for Hardware Security Assurance
- Inside the SiFive Performance™ P570 Gen 3: High Performance Efficiency for Next-Generation Consumer and Commercial Applications
- What the steam engine can teach us about modern chip design
- Automotive silicon in the era of AI, functional safety, and cybersecurity
- JPEG XS Officially Joins GenICam, The Machine Vision Standard Managed By EMVA