Abstract
Snappy is a widely used (de) compression algorithm in many big data applications. Such a data compression technique has been proven to be successful to save storage space and to reduce the amount of data transmission from/to storage devices. In this paper, we present a fine-grained parallel Snappy decompressor on FPGAs running under a relaxed execution model that addresses the following main challenges in existing solutions. First, existing designs either can only process one token per cycle or can process multiple tokens per cycle with low area efficiency and/or low clock frequency. Second, the high read-after-write data dependency during decompression introduces stalls which pull down the throughput.
Original language | English |
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Title of host publication | 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) |
Subtitle of host publication | Proceedings |
Publisher | IEEE |
Pages | 335-335 |
Number of pages | 1 |
ISBN (Electronic) | 978-1-7281-1131-5 |
ISBN (Print) | 978-1-7281-1132-2 |
DOIs | |
Publication status | Published - 2019 |
Event | 27th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2019 - San Diego, United States Duration: 28 Apr 2019 → 1 May 2019 |
Conference
Conference | 27th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2019 |
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Country/Territory | United States |
City | San Diego |
Period | 28/04/19 → 1/05/19 |
Keywords
- FPGA
- Fine grained Parallelism
- Snappy Decompressor