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    Booth Recoding Multiplier

    by adrian-structure

    1

    Radix-4 Booth multiplier design with verified RTL and an exhaustive golden model — halve your partial products without sign-extension bugs.

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    See it in action

    You say

    Generate a 16-bit signed Radix-4 Booth multiplier in Verilog with the corresponding Python golden model for verification.

    Your agent does

    RTL generated: booth_mult_16x16.v (parameterized). Logic: Radix-4 recoding, 8 partial products, MSB inversion for sign-prevention. Correction Vector C: 0xAAAA8000. Run python3 scripts/booth_golden.py --width 16 to verify against hardware-accurate partial products.

    What you get

    Design a radix-4 Booth multiplier in Verilog without sign-extension bugsVerify a multiplier implementation against an exhaustive bit-exact golden modelDecide between Booth recoding and plain AND-array partial products on FPGA

    About this skill

    Build radix-4 (modified Booth) multipliers that actually work on the first synthesis.

    What you get:

    • The complete radix-4 recoding tables and encoder equations, with the sign-extension-prevention correction vector derived in closed form — the part most implementations get wrong.
    • One's-complement negative partial products with separate correction bits, signed AND unsigned operand handling (including the extra top digit unsigned operands need).
    • Parameterized Verilog-2001: digit encoder, partial-product generator, and a full generate-loop multiplier — compiled and simulated with 1,515 checks, 0 errors, including the INT_MIN x INT_MIN corner.
    • A pure-stdlib Python golden model that reproduces the hardware algorithm bit-exactly (rows + neg bits + correction vector, not a shortcut a*b) — verified exhaustively on all 65,536 signed 8x8 pairs plus random 32x32, with a debug CLI that prints the recoded digits for any operand pair.
    • An honest FPGA-vs-ASIC section: when Booth pays off on LUT fabrics and when a plain AND-array partial-product multiplier is the better choice.

    Run python3 scripts/booth_golden.py after install: it self-tests in seconds and exits 0. Every claim in this skill is backed by that runnable evidence.

    Changelog

    1. v1.1LatestAug 8, 2026

      Updated skill file

    2. v1.0Aug 8, 2026

      Initial release

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Creator

    adrian-structure
    adrian-structure

    4 skills on Agensi

    We sell high-quality skills only – not the standard knowledge any language model already has. Every skill is tested and reviewed by us before it's listed. Our goal: using our skills should let you build your own business and actually make money. Wir verkaufen ausschließlich hochwertige Skills – keine Standard-Antworten, die ohnehin jedes Sprachmodell liefert. Jeder Skill wird von uns getestet und geprüft, bevor er hier gelistet wird. Unser Anspruch: Mit unseren Skills sollst du dein eigenes Geschäft aufbauen und wirklich Geld verdienen können.

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