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LDPC (Low-Density Parity-Check) codes have become a cornerstone of transforming a noise-filled physical channel into a reliable and high-performance data channel in communication and storage systems. FPGA (Field-Programmable Gate Array) based LDPC hardware, especially for decoding with high complexity, is essential to realizing the high-bandwidth channel prototypes. HLS (High-Level Synthesis) is introduced to speed up the FPGA development of LDPC hardware by automatically compiling high-level abstract behavioral descriptions into RTL-level implementations, but often sub-optimally due to lacking effective low-level descriptions. To overcome this problem, this paper proposes an HLS-friendly QC-LDPC FPGA decoder architecture, HF-LDPC, that employs HLS not only to precisely characterize high-level behaviors but also to effectively optimize low-level RTL implementation, thus achieving both high throughput and flexibility. First, HF-LDPC designs a multi-unit framework with a balanced I/O-computing dataflow to adaptively match code parameters with FPGA configurations. Second, HFLDPC presents a novel fine-grained task-level pipeline with interleaved updating to eliminate stalls due to data interdependence within each updating task. HF-LDPC also presents several HLSenhanced approaches. We implement and evaluate HF-LDPC on Xilinx U50, which demonstrates that HF-LDPC outperforms existing implementations by 4× to 84× with the same parameter and linearly scales to up to 116 Gbps actual decoding throughput with high hardware efficiency.
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FPGA Acceleration of Probabilistic Sentential Decision Diagrams with High-level Synthesis
Probabilistic Sentential Decision Diagrams (PSDDs) provide efficient methods for modeling and reasoning with probability distributions in the presence of massive logical constraints. PSDDs can also be synthesized from graphical models such as Bayesian networks (BNs) therefore offering a new set of tools for performing inference on these models (in time linear in the PSDD size). Despite these favorable characteristics of PSDDs, we have found multiple challenges in PSDD’s FPGA acceleration. Problems include limited parallelism, data dependency, and small pipeline iterations. In this article, we propose several optimization techniques to solve these issues with novel pipeline scheduling and parallelization schemes. We designed the PSDD kernel with a high-level synthesis (HLS) tool for ease of implementation and verified it on the Xilinx Alveo U250 board. Experimental results show that our methods improve the baseline FPGA HLS implementation performance by 2,200X and the multicore CPU implementation by 20X. The proposed design also outperforms state-of-the-art BN and Sum Product Network (SPN) accelerators that store the graph information in memory.
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- Award ID(s):
- 1937599
- PAR ID:
- 10464891
- Date Published:
- Journal Name:
- ACM Transactions on Reconfigurable Technology and Systems
- Volume:
- 16
- Issue:
- 2
- ISSN:
- 1936-7406
- Page Range / eLocation ID:
- 1 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3561514
