Abstract
General purpose hardware accelerators have become major data processing resources in many computing domains. However, the processing capability of hardware accelerations is often limited by costly software interventions and memory copies to support compulsory data movement between different processors and solid-state drives (SSDs). This in turn also wastes a significant amount of energy in modern accelerated systems. In this work, we propose, DRAM-less, a hardware automation approach that precisely integrates many state-of-the-art phase change memory (PRAM) modules into its data processing network to dramatically reduce unnecessary data copies with a minimum of software modifications. We implement a new memory controller that plugs a real 3x nm multi-partition PRAM to 28nm technology FPGA logic cells and interoperate its design into a real PCIe accelerator emulation platform. The evaluation results reveal that our DRAM-less achieves, on average, 47% better performance than advanced acceleration approaches that use a peer-to-peer DMA.
Original language | English |
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Title of host publication | Proceedings - 2020 IEEE International Symposium on High Performance Computer Architecture, HPCA 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 287-302 |
Number of pages | 16 |
ISBN (Electronic) | 9781728161495 |
DOIs | |
Publication status | Published - 2020 Feb |
Event | 26th IEEE International Symposium on High Performance Computer Architecture, HPCA 2020 - San Diego, United States Duration: 2020 Feb 22 → 2020 Feb 26 |
Publication series
Name | Proceedings - 2020 IEEE International Symposium on High Performance Computer Architecture, HPCA 2020 |
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Conference
Conference | 26th IEEE International Symposium on High Performance Computer Architecture, HPCA 2020 |
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Country/Territory | United States |
City | San Diego |
Period | 20/2/22 → 20/2/26 |
Bibliographical note
Funding Information:The authors thank Dr. Hung-Wei Tseng for shepherding their paper. This paper is a full-length version of an earlier 4-page letter [69]. This research is mainly supported by NRF 2016R1C182015312, MemRay grant (G01190170), DOE DEAC02-05CH11231, NRF 2015M3C4A7065645, and KAIST start-up package (G01190015). J. Zhang and G. Park equally contribute to this work. Myoungsoo Jung is the corresponding author.
Publisher Copyright:
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Hardware and Architecture
- Safety, Risk, Reliability and Quality