#!/bin/sh exec scala "$0" "$@" !# import scala.util.{Success, Try} import scala.util.matching.Regex val port = raw"\s*(input|output|inout)\s+(wire\s*|reg\s*)?\s*(\[(\d+):(\d+)\])?\s*(\w+)?\s*;?".r val p = scala.io.Source.fromFile(args(0)).getLines.foreach { f: String => val b = f match { case port(d, tzpe, _, msb, lsb, name) => val dir = d match { case "input" => "Input" case "output" => "Output" case "inout" => "Analog" case _ => } val w = Try({msb.toInt - lsb.toInt + 1}) match { case Success(s) => s"%d".W.format(s) case _ => "1.W" } s"val $name = $dir(UInt($w))" case _ => "" } if (b.nonEmpty) println(b) }
The chosen regular expression was testing using regex101.com, a populate online regular expression tester.
The article presents a practical solution for automating the conversion of Verilog module interfaces into Chisel-compatible port declarations. By leveraging Scala and regular expressions, the script eliminates repetitive manual work when integrating existing Verilog designs as Chisel black boxes. The example demonstrates how even a relatively small automation utility can significantly improve productivity and reduce the chances of introducing interface-related errors during hardware development.
ReplyDeleteA particularly interesting aspect of the script is its ability to automatically generate new source code from existing hardware descriptions. Rather than manually rewriting port definitions, developers can use automation to transform one representation into another with minimal effort. Such code-generation workflows closely align with Generative AI Projects for Final Year, where systems are designed to generate structured outputs, automate development tasks, and improve engineering efficiency.
The use of regular expressions to parse hardware descriptions and produce formatted Chisel declarations is a great example of structured content generation. Converting design metadata into reusable code artifacts helps engineers accelerate development and maintain consistency across projects. Similar techniques can be explored through Code Generation Projects, where automated generation systems transform input specifications into meaningful textual outputs.
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