AI Article Synopsis

  • Computationally hard combinatorial optimization problems (COPs) are common in various applications, and existing solutions like digital annealers and quantum systems face issues related to memory, scalability, and applicability.
  • The study introduces a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer designed to address these challenges by converting COPs into quadratic unconstrained binary optimization (QUBO) formats and accelerating key calculations.
  • Experimental results show that this FeFET CiM annealer can significantly reduce chip size by 75% for Max-Cut problems, while also delivering up to 27% better solutions and doubling the speed compared to traditional simulated annealing methods.

Article Abstract

Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications. Various digital annealers, dynamical Ising machines, and quantum/photonic systems have been developed for solving COPs, but they still suffer from the memory access issue, scalability, restricted applicability to certain types of COPs, and VLSI-incompatibility, respectively. Here we report a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer for solving larger-scale COPs efficiently. Our CiM annealer converts COPs into quadratic unconstrained binary optimization (QUBO) formulations, and uniquely accelerates in-situ the core vector-matrix-vector (VMV) multiplication operations of QUBO formulations in a single step. Specifically, the three-terminal FeFET structure allows for lossless compression of the stored QUBO matrix, achieving a remarkably 75% chip size saving when solving Max-Cut problems. A multi-epoch simulated annealing (MESA) algorithm is proposed for efficient annealing, achieving up to 27% better solution and ~ 2X speedup than conventional simulated annealing. Experimental validation is performed using the first integrated FeFET chip on 28nm HKMG CMOS technology, indicating great promise of FeFET CiM array in solving general COPs.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10948773PMC
http://dx.doi.org/10.1038/s41467-024-46640-xDOI Listing

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