Abstract
Computation-in-Memory (CiM) is a new computer architecture template based on the in-memory computing paradigm. CiM can solve the memory-wall problem of classical Von Neumann-based computer systems by exploiting application-specific computational and data-flow patterns with the capability of performing both storage and computations of emerging resistive RAM technologies (e.g., memristors). However, to efficiently explore and design such radically new application-specific CiM architectures, we require fundamentally new algorithm specification and compilation techniques. In this paper, we introduce a domain-specific language to express not only the computational patterns of an algorithm but also its spatial characteristics. Furthermore, we design a compiler that is able to transform these patterns into highly-optimized CiM designs. Experiments demonstrate the functional correctness of the language and the compiler as well as an order of magnitude speedup improvement over a multicore system in both performance and energy costs.
Original language | English |
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Title of host publication | GLSVLSI '17 Proceedings of the on Great Lakes Symposium on VLSI 2017 |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 71-76 |
Number of pages | 6 |
ISBN (Print) | 978-1-4503-4972-7 |
DOIs | |
Publication status | Published - 2017 |
Event | 27th ACM Great Lakes Symposium on VLSI ((GLSVLSI) - Banff, Canada Duration: 10 May 2017 → 12 May 2017 Conference number: 2017 http://www.glsvlsi.org/ |
Conference
Conference | 27th ACM Great Lakes Symposium on VLSI ((GLSVLSI) |
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Abbreviated title | GLSVLSI 2017 |
Country/Territory | Canada |
City | Banff |
Period | 10/05/17 → 12/05/17 |
Internet address |
Keywords
- Domain specific language
- Memristors
- Computation-in-memory
- Algorithmic skeleton