Industry Expert Blogs

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.