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CMOS Annealing Machine: an In-memory Computing Accelerator to Process Combinatorial Optimization Problems

机译：CMOS退火机：用于处理组合优化问题的内存计算加速器

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A new computing architecture, an annealing machine, which is specialized to solve combinatorial optimization problems, is proposed. The annealing machine maps optimization problems to an Ising model and solves the optimization problems by its own convergence property. We proposed a CMOS implementation of the annealing machine, which is a type of an in-memory computing. We constructed two prototypes of the CMOS annealing machine. The 1st-generation prototype confirmed the power efficiency is 1800times higher than that of the conventional von-Neumann computers. The FPGA 2md-generation prototype is used to explore its applications and is also confirmed its multiple-chip operation.

机译：提出了一种新的计算架构，即退火机，专门用于解决组合优化问题。退火机将优化问题映射到Ising模型，并通过其自身的收敛性来解决优化问题。我们提出了退火机的CMOS实现，这是一种内存中计算。我们构造了CMOS退火机的两个原型。第一代原型机的功率效率比传统的von-Neumann计算机高1800倍。 FPGA 2md代原型被用于探索其应用，并且还证实了其多芯片操作。

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《IEEE Custom Integrated Circuits Conference》|2019年|1-8|共8页
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Masanao Yamaoka; Takuya Okuyama; Masato Hayashi; Chihiro Yoshimura; Takashi Takemoto;
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Annealing; Semiconductor device modeling; Optimization; Computational modeling; Prototypes; Integrated circuit modeling; Computers;

机译：退火;半导体器件建模;优化;计算建模;原型;集成电路建模;计算机;

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专利

1. A 2 × 30k-Spin Multi-Chip Scalable CMOS Annealing Processor Based on a Processing-in-Memory Approach for Solving Large-Scale Combinatorial Optimization Problems [J] . Takemoto Takashi, Hayashi Masato, Yoshimura Chihiro, IEEE Journal of Solid-State Circuits . 2020,第1期

机译：基于处理大规模组合优化问题的加工内存方法，2×30k-旋转多芯片可扩展CMOS退火处理器
2. Circuits and Architectures for In-Memory Computing-Based Machine Learning Accelerators [J] . Ankit Aayush, Chakraborty Indranil, Agrawal Amogh, IEEE Micro . 2020,第6期

机译：基于内存计算的机器学习加速器的电路和架构
3. Monolithically Integrated RRAM- and CMOS-Based In-Memory Computing Optimizations for Efficient Deep Learning [J] . Yin Shihui, Kim Yulhwa, Han Xu, IEEE Micro . 2019,第6期

机译：基于单片集成的RRAM和CMOS的内存中计算优化，可实现高效的深度学习
4. CMOS Annealing Machine: an In-memory Computing Accelerator to Process Combinatorial Optimization Problems [C] . Masanao Yamaoka, Takuya Okuyama, Masato Hayashi, IEEE Custom Integrated Circuits Conference . 2019

机译：CMOS退火机：内存计算加速器，用于处理组合优化问题
5. Performance and Power Prediction of Compute Accelerators Using Machine Learning [D] . O'Neal, Kenneth Norman Lee. 2018

机译：基于机器学习的计算加速器的性能和功率预测
6. A RISC-V Processor with Area-Efficient Memristor-Based In-Memory Computing for Hash Algorithm in Blockchain Applications [O] . Xiaoyong Xue, Chenzedai Wang, Wenjun Liu, 2019

机译：具有基于面积忆阻器的内存计算的RISC-V处理器用于区块链应用中的哈希算法
7. Semiconductor CMOS Annealing Machine Accelerating Combinatorial Optimization Processing [O] . Masanao YAMAOKA 2019

机译：半导体CMOS退火机加速组合优化处理
8. TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization. [R] . Hamacher, H. W., Goerigk, M. 2016

机译：TaRCmO：鲁棒，组合，多准则优化的理论和算法。

CMOS Annealing Machine: an In-memory Computing Accelerator to Process Combinatorial Optimization Problems

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