RSQP: Problem-specific Architectural Customization for Accelerated Convex Quadratic Optimization-Reference-Cited by-同舟云学术

RSQP: Problem-specific Architectural Customization for Accelerated Convex Quadratic Optimization

Published:2023-06-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 50th Annual International Symposium on Computer Architecture
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Author:

Wang Maolin¹^ORCID,McInerney Ian²^ORCID,Stellato Bartolomeo³^ORCID,Boyd Stephen⁴^ORCID,So Hayden Kwok-Hay⁵^ORCID

Affiliation:

1. AI Chip Center for Emerging Smart Systems, Hong Kong, Hong Kong

2. The University of Manchester, Manchester, United Kingdom

3. Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey, USA

4. Stanford University, Stanford, California, USA

5. University of Hong Kong, Hong Kong, Hong Kong

Publisher

ACM

Reference33 articles.

1. A Survey of Parallel Implementations for Model Predictive Control

2. A. Agrawal B. Amos S. Barratt S. Boyd S. Diamond and Z. Kolter. 2019. Differentiable Convex Optimization Layers. In Advances in Neural Information Processing Systems. A. Agrawal B. Amos S. Barratt S. Boyd S. Diamond and Z. Kolter. 2019. Differentiable Convex Optimization Layers. In Advances in Neural Information Processing Systems.

3. Brandon Amos and J Zico Kolter . 2017 . Optnet: Differentiable optimization as a layer in neural networks . In International Conference on Machine Learning. PMLR, 136--145 . Brandon Amos and J Zico Kolter. 2017. Optnet: Differentiable optimization as a layer in neural networks. In International Conference on Machine Learning. PMLR, 136--145.

4. Optimising Memory Bandwidth Use for Matrix-Vector Multiplication in Iterative Methods

5. Stephen Boyd Enzo Busseti Steve Diamond Ronald N Kahn Kwangmoo Koh Peter Nystrup Jan Speth etal 2017. Multi-period trading via convex optimization. Foundations and Trends® in Optimization 3 1 (2017) 1--76. Stephen Boyd Enzo Busseti Steve Diamond Ronald N Kahn Kwangmoo Koh Peter Nystrup Jan Speth et al. 2017. Multi-period trading via convex optimization. Foundations and Trends® in Optimization 3 1 (2017) 1--76.

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