VLDB 2026 Research / reviewers in the wild / expert
Paul A. Crowell
dblp:156/1931
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2017
0000-0002-1163-9614ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
Emerging computing paradigms · 68% Memory systems · 22% Integrated circuit design · 8% |
Topics — the 11 heaviest of 11, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Emerging computing paradigms
beyond-CMOS computing |
0.3 | 1 | 2017 | A Pathway to Enable Exponential Scaling for the Beyond-CMOS Era: Invited · DAC 2017 |
Emerging computing paradigms › neuromorphic computing
cognitive computing |
0.3 | 1 | 2017 | A Pathway to Enable Exponential Scaling for the Beyond-CMOS Era: Invited · DAC 2017 |
Memory systems › processing-in-memory
logic-in-memory |
0.3 | 1 | 2017 | A Pathway to Enable Exponential Scaling for the Beyond-CMOS Era: Invited · DAC 2017 |
Emerging computing paradigms
neuromorphic computing |
0.3 | 1 | 2017 | A Pathway to Enable Exponential Scaling for the Beyond-CMOS Era: Invited · DAC 2017 |
Emerging computing paradigms › approximate and stochastic computing
probabilistic computing |
0.3 | 1 | 2017 | A Pathway to Enable Exponential Scaling for the Beyond-CMOS Era: Invited · DAC 2017 |
Memory systems › emerging memory technologies
spintronic memory |
0.3 | 1 | 2017 | A Pathway to Enable Exponential Scaling for the Beyond-CMOS Era: Invited · DAC 2017 |
Emerging computing paradigms › spintronics › spintronic computing
all-spin logic |
0.2 | 1 | 2015 | Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015 |
Emerging computing paradigms › beyond-CMOS computing
beyond-CMOS devices |
0.2 | 1 | 2015 | Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015 |
Integrated circuit design
low-power circuit design |
0.2 | 1 | 2015 | Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015 |
Emerging computing paradigms
spintronics |
0.2 | 1 | 2015 | Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015 |
Processor architecture and microarchitecture
microprocessor design |
0.1 | 1 | 2015 | Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015 |
Methods — techniques the papers use, named apart from their topics
system-level power simulation · 0.2device modeling · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | A Pathway to Enable Exponential Scaling for the Beyond-CMOS Era: InvitedabstractMany key technologies of our society, including so-called artificial intelligence (AI) and big data, have been enabled by the invention of transistor and its ever-decreasing size and ever-increasing integration at a large scale. However, conventional technologies are confronted with a clear scaling limit. Many recently proposed advanced transistor concepts are also facing an uphill battle in the lab because of necessary performance tradeoffs and limited scaling potential. We argue for a new pathway that could enable exponential scaling for multiple generations. This pathway involves layering multiple technologies that enable new functions beyond those available from conventional and newly proposed transistors. The key principles for this new pathway have been demonstrated through an interdisciplinary team effort at C-SPIN (a STARnet center), where systems designers, device builders, materials scientists and physicists have all worked under one umbrella to overcome key technology barriers. This paper reviews several successful outcomes from this effort on topics such as the spin memory, logic-in-memory, cognitive computing, stochastic and probabilistic computing and reconfigurable information processing. Jianping Wang 0006, Sachin S. Sapatnekar, Chris H. Kim, Paul A. Crowell, Steven J. Koester, Supriyo Datta, Kaushik Roy 0001, Anand Raghunathan, Xiaobo Sharon Hu, Michael T. Niemier, Azad Naeemi, Chia-Ling Chien, Caroline A. Ross, Roland Kawakami |
DAC | 4 |
| 2015 | Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance MicroprocessorabstractAs the end draws near for Moore's law, the search for low-power alternatives to complementary metal-oxide-semiconductor (CMOS) technology is intensifying. Among the various post-CMOS candidates, spintronic devices have gained special attention for their potential to overcome the power and performance limitations of CMOS. In particular, all spin logic (ASL) technology, which performs Boolean operations and transfers the output in the spin domain, has been proposed for enabling new capabilities-such as high density, low device count, and nonvolatility-that were previously impossible with CMOS technology. In this paper, first we provide an overview of the history and the current status of the various spintronic devices being pursued by the research community. Then, we describe how spin-based components are integrated into a computing system and the advantages that result. We use a hypothetical spintronic-based Intel Core i7 as a test vehicle to compare the system-level power requirements of ASL- and CMOS-based systems, taking into consideration the unique demands of spin-based interconnects. We conclude with a brief analysis of current limitations and future directions of spintronic research. Jongyeon Kim, Ayan Paul, Paul A. Crowell, Steven J. Koester, Sachin S. Sapatnekar, Jianping Wang 0006, Chris H. Kim |
Proc. IEEE | 3 |