Autogeneration of Pipelined Belief Propagation Polar Decoders
Chao Ji, Yifei Shen, Zaichen Zhang, Xiaohu You, Chuan Zhang · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2020
Though belief propagation (BP) polar decoders can achieve higher throughput than successive-cancellation (SC)-based decoders, and how to efficiently generate different belief propagation decoders (BPDs) which can meet various design specifications remains challenging. To this end, an autogeneration, which can translate the generation formula of BPDs to efficient hardware implementations, has been proposed in this article. For different requirements, two BPD architectures have been given: 1) low-cost decoder (Type-I) and 2) high-throughput decoder (Type-II). The autogeneration of them can support different code rates, code lengths, and parallelisms. Synthesis results show that Type-I and Type-II provide higher throughput and hardware efficiency than the state-of-the-art (SOA) SC decoders. Moreover, compared to the SOA BPDs, both Type-I and Type-II achieve similar even better energy- and area-efficiency with a comparable throughput, for fully parallel configuration. With the autogeneration, we are able to obtain the design space regarding different design metrics, such as area efficiency, energy efficiency, and power density, within which the design optimization under given design constraints can be conducted.