Stochastic Computing (SC) is an attractive solution for implementing Low Density Parity Codes (LDPC) decoders due to its fault tolerance capability and low hardware requirements. However, in practical implementations, SC efficiency is limited by the Stochastic Bitstream (SB) length and by the computation inaccuracies due to non-unique SB representations. In this paper, rather than statically fixing the SB length at run-time, we propose a Dynamic Bitstream Length Scaling (DBLS) technique, which

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... djusts on-the-fly the SB length such that Quality of Service requirements for energy efficient LDPC decoding are fulfilled. In this way, depending on the communication channel condition, different SB lengths are adaptively utilized such that the best decoding performance vs energy consumption tradeoff is achieved. To evaluate the DBLS practical implications we selected an (1296,648) LDPC with dv = 3 and dc = 6 and implemented our approach and the best state-of-the-art stochastic LDPC decoder with 64-bit edge memory on a Virtex-7 FPGA. Experimental results indicate that our proposal requires 9% more FFs and 3% more LUTs while diminishing the energy consumption by 31-80% and providing 1.5-5.1x higher throughput.