We present novel architectures for parallel-node low-density parity-check convolutional code (PN-LDPC-CC) encoders and decoders. Based on a recently introduced implementation-aware class of LDPC-CCs, these encoders and decoders take advantage of increased node-parallelization to simultaneously decrease the energy-per-bit and increase the decoded information throughput. A series of progressively improved encoder and decoder designs are presented and characterized using synthesis results with respect to power, area and throughput. The best of the encoder and decoder designs significantly advance the state-of-the-art in terms of both the energy-per-bit and throughput/area metrics. One of the presented decoders, for an Eb/N0 of 2.5 dB has a bit-error-rate of 10-6, takes 4.5 mm2 in a CMOS 90-nm process, and achieves an energy-per-decoded-information-bit of 65 pJ and a decoded information throughput of 4.8 Gbits/s. We implement an earlier non-parallel node LDPC-CC encoder, decoder and a channel emulator in silicon. We provide readers, via two sets of tables, the ability to look up our decoder hardware metrics, across four different process technologies, for over 1000 variations of our PN-LDPC-CC decoders. By imposing practical decoder implementation constraints on power or area, which in turn drives trade-offs in code size versus the number of decoder processors, we compare the code BER performance. An extensive comparison to known LDPC-BC/CC decoder implementations is provided.
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