FPIA: Field-Programmable Ising Arrays with In-Memory Computing
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914657364606976 |
|---|---|
| author | Hutchinson, George Higgins Sifferman, Ethan Bhattacharya, Tinish Strukov, Dmitri B. |
| author_facet | Hutchinson, George Higgins Sifferman, Ethan Bhattacharya, Tinish Strukov, Dmitri B. |
| contents | Ising Machine is a promising computing approach for solving combinatorial optimization problems. It is naturally suited for energy-saving and compact in-memory computing implementations with emerging memories. A naïve in-memory computing implementation of a quadratic Ising Machine requires an array of coupling weights that grows quadratically with problem size. However, the resources in such an approach are used inefficiently due to sparsity in practical optimization problems. We first show that this issue can be addressed by partitioning a coupling array into smaller sub-arrays. This technique, however, requires interconnecting subarrays; hence, we developed in-memory computing architecture for quadratic Ising Machines inspired by island-type field programmable gate arrays, which is the main contribution of our paper. We adapt open-source tools to optimize problem embedding and model routing overhead. Modeling results of benchmark problems for the developed architecture show up to 60x area improvement and faster operation than the baseline approach. Finally, we discuss algorithm/circuit co-design techniques for further improvements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16202 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FPIA: Field-Programmable Ising Arrays with In-Memory Computing Hutchinson, George Higgins Sifferman, Ethan Bhattacharya, Tinish Strukov, Dmitri B. Hardware Architecture Emerging Technologies Ising Machine is a promising computing approach for solving combinatorial optimization problems. It is naturally suited for energy-saving and compact in-memory computing implementations with emerging memories. A naïve in-memory computing implementation of a quadratic Ising Machine requires an array of coupling weights that grows quadratically with problem size. However, the resources in such an approach are used inefficiently due to sparsity in practical optimization problems. We first show that this issue can be addressed by partitioning a coupling array into smaller sub-arrays. This technique, however, requires interconnecting subarrays; hence, we developed in-memory computing architecture for quadratic Ising Machines inspired by island-type field programmable gate arrays, which is the main contribution of our paper. We adapt open-source tools to optimize problem embedding and model routing overhead. Modeling results of benchmark problems for the developed architecture show up to 60x area improvement and faster operation than the baseline approach. Finally, we discuss algorithm/circuit co-design techniques for further improvements. |
| title | FPIA: Field-Programmable Ising Arrays with In-Memory Computing |
| topic | Hardware Architecture Emerging Technologies |
| url | https://arxiv.org/abs/2401.16202 |