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PLDI2026顶会

Fungible Memories for Automated Technology Mapping and Retargeting

Zachary D. Sisco, Sijie Kong, Daniel Ruelas-Petrisko, Jingtao Xia, Julian Springer, Varun Rao, Spencer Wang, Gus Henry Smith, Ben Hardekopf, Jonathan Balkind

2026年份

摘要

During chip development, engineers must target different technologies, such as simulation and various ASIC and FPGA technologies. Conventionally, they split parts of the code (e.g., memories) into separate technology-specialized blocks implementing the same high-level behavior. This leads to brittle code, with multiple but subtly different blocks describing the same semantic behavior, harming verification, agility, and extensibility. We propose fungible memories , an HDL-level “write once, map anywhere” memory abstraction with rich enough semantics to automatically target all relevant technologies using a single generic interface. We incorporate fungible memories into a compiler called Memo . For designs without a specific technology mapping, we also present a memory decompiler which lifts memories from an existing gate-level design to Memo , enabling automated technology retargeting, which is a holy grail for digital designers. We present a structure-aware equality saturation technique which scales to netlists with millions of cells and identifies memories that the state of the art cannot. We demonstrate that Memo effectively targets backends across different technology platforms (simulation, ASIC, and FPGA) over a suite of representative designs, including a RISC-V multicore SoC.

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