CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916180840677376 |
|---|---|
| author | Singh, Nihal Sanjay Kobayashi, Keito Cao, Qixuan Selcuk, Kemal Hu, Tianrui Niazi, Shaila Aadit, Navid Anjum Kanai, Shun Ohno, Hideo Fukami, Shunsuke Camsari, Kerem Y. |
| author_facet | Singh, Nihal Sanjay Kobayashi, Keito Cao, Qixuan Selcuk, Kemal Hu, Tianrui Niazi, Shaila Aadit, Navid Anjum Kanai, Shun Ohno, Hideo Fukami, Shunsuke Camsari, Kerem Y. |
| contents | Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_05949 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning Singh, Nihal Sanjay Kobayashi, Keito Cao, Qixuan Selcuk, Kemal Hu, Tianrui Niazi, Shaila Aadit, Navid Anjum Kanai, Shun Ohno, Hideo Fukami, Shunsuke Camsari, Kerem Y. Mesoscale and Nanoscale Physics Artificial Intelligence Emerging Technologies Machine Learning Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency. |
| title | CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning |
| topic | Mesoscale and Nanoscale Physics Artificial Intelligence Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2304.05949 |