BRIM: Bistable Resistively-Coupled Ising Machine
Richard Afoakwa, Yiqiao Zhang, Uday Kumar Reddy Vengalam, Zeljko Ignjatovic, Michael C. Huang
Abstract
Physical Ising machines rely on nature to guide a dynamical system towards an optimal state which can be read out as a heuristical solution to a combinatorial optimization problem. Such designs that use nature as a computing mechanism can lead to higher performance and/or lower operation costs. Quantum annealers are a prominent example of such efforts. However, existing Ising machines are generally bulky and energy intensive. Such disadvantages may be acceptable if these designs provide some significant intrinsic advantages at a much larger scale in the future, which remains to be seen. But for now, integrated electronic designs of Ising machines allow more immediate applications. We propose one such design that uses bistable nodes, coupled with programmable and variable strengths. The design is fully CMOS compatible for on-chip applications and demonstrates competitive solution quality and significantly superior execution time and energy.
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Cited by top-tier papers12
- Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under UncertaintyZhenyu Pan, Anshujit Sharma, Jerry Yao-Chieh Hu, Zhuo Liu et al.AAAI 2023 · 43 citations
- Increasing ising machine capacity with multi-chip architecturesAnshujit Sharma, Richard Afoakwa, Zeljko Ignjatovic, Michael C. HuangISCA 2022 · 28 citations
- Ising-CF: A Pathbreaking Collaborative Filtering Method Through Efficient Ising Machine LearningZhuo Liu, Yunan Yang, Zhenyu Pan, Anshujit Sharma et al.DAC 2023 · 18 citations
- SACHI: A Stationarity-Aware, All-Digital, Near-Memory, Ising ArchitectureSiddhartha Raman Sundara Raman, Lizy K. John, Jaydeep P. KulkarniHPCA 2024 · 11 citations
- Supporting Energy-based Learning with an Ising Machine substrate: a Case Study on RBMUday Kumar Reddy Vengalam, Yongchao Liu, Tong Geng, Hui Wu et al.MICRO 2023 · 8 citations
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