Enabling Reusable Physical Design Flows with Modular Flow Generators

TL;DR

This paper introduces a modular flow generator framework that enhances code reuse in physical design flows by using Python-based instrumentation, static assertions, and flow consistency checks, demonstrated across multiple silicon technologies.

Contribution

The paper presents a novel modular flow generator framework with embedded Python instrumentation and static assertions to improve code reuse and flow customization in physical design.

Findings

Enables significant code reuse across multiple silicon technologies.

Supports early feedback and consistency checks in design flows.

Gradually types Tcl language for better static analysis.

Abstract

Achieving high code reuse in physical design flows is challenging but increasingly necessary to build complex systems. Unfortunately, existing approaches based on parameterized Tcl generators support very limited reuse and struggle to preserve reusable code as designers customize flows for specific designs and technologies. We present a vision and framework based on modular flow generators that encapsulates coarse-grain and fine-grain reusable code in modular nodes and assembles them into complete flows. The key feature is a flow consistency and instrumentation layer embedded in Python, which supports mechanisms for rapid and early feedback on inconsistent composition. The approach gradually types the Tcl language and allows both automatic and user-annotated static assertion checks. We evaluate the design flows of successive generations of silicon prototypes designed in TSMC16, TSMC28, TSMC40, SKY130, and IBM180 technologies, showing how our approach can enable significant code reuse in future flows.