Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911104598278144 |
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| author | Duffee, Christian Athas, Jordan Shao, Yixin Melendez, Noraica Davila Raimondo, Eleonora Katine, Jordan A. Camsari, Kerem Y. Finocchio, Giovanni Amiri, Pedram Khalili |
| author_facet | Duffee, Christian Athas, Jordan Shao, Yixin Melendez, Noraica Davila Raimondo, Eleonora Katine, Jordan A. Camsari, Kerem Y. Finocchio, Giovanni Amiri, Pedram Khalili |
| contents | Probabilistic Ising machines (PIMs) provide a path to solving many computationally hard problems more efficiently than deterministic algorithms on von Neumann computers. Stochastic magnetic tunnel junctions (S-MTJs), which are engineered to be thermally unstable, show promise as entropy sources in PIMs. However, scaling up S-MTJ-PIMs is challenging, as it requires fine control of a small magnetic energy barrier across large numbers of devices. In addition, non-spintronic components of S-MTJ-PIMs to date have been primarily realized using general-purpose processors or field-programmable gate arrays. Reaching the ultimate performance of spintronic PIMs, however, requires co-designed application-specific integrated circuits (ASICs), combining CMOS with spintronic entropy sources. Here we demonstrate an ASIC in 130 nm foundry CMOS, which implements integer factorization as a representative hard optimization problem, using PIM-based invertible logic gates realized with 1143 probabilistic bits. The ASIC uses stochastic bit sequences read from an adjacent voltage-controlled (V-) MTJ chip. The V-MTJs are designed to be thermally stable in the absence of voltage, and generate random bits on-demand in response to 10 ns pulses using the voltage-controlled magnetic anisotropy effect. We experimentally demonstrate the chip's functionality and provide projections for designs in advanced nodes, illustrating a path to millions of probabilistic bits on a single CMOS+V-MTJ chip. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08017 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source Duffee, Christian Athas, Jordan Shao, Yixin Melendez, Noraica Davila Raimondo, Eleonora Katine, Jordan A. Camsari, Kerem Y. Finocchio, Giovanni Amiri, Pedram Khalili Applied Physics Disordered Systems and Neural Networks Probabilistic Ising machines (PIMs) provide a path to solving many computationally hard problems more efficiently than deterministic algorithms on von Neumann computers. Stochastic magnetic tunnel junctions (S-MTJs), which are engineered to be thermally unstable, show promise as entropy sources in PIMs. However, scaling up S-MTJ-PIMs is challenging, as it requires fine control of a small magnetic energy barrier across large numbers of devices. In addition, non-spintronic components of S-MTJ-PIMs to date have been primarily realized using general-purpose processors or field-programmable gate arrays. Reaching the ultimate performance of spintronic PIMs, however, requires co-designed application-specific integrated circuits (ASICs), combining CMOS with spintronic entropy sources. Here we demonstrate an ASIC in 130 nm foundry CMOS, which implements integer factorization as a representative hard optimization problem, using PIM-based invertible logic gates realized with 1143 probabilistic bits. The ASIC uses stochastic bit sequences read from an adjacent voltage-controlled (V-) MTJ chip. The V-MTJs are designed to be thermally stable in the absence of voltage, and generate random bits on-demand in response to 10 ns pulses using the voltage-controlled magnetic anisotropy effect. We experimentally demonstrate the chip's functionality and provide projections for designs in advanced nodes, illustrating a path to millions of probabilistic bits on a single CMOS+V-MTJ chip. |
| title | Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source |
| topic | Applied Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2412.08017 |