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Guessing Random Additive Noise Decoding (GRAND) is a universal decodingalgorithm that has been recently proposed as a practical way to perform maximumlikelihood decoding. It generates a sequence of possible error patterns andapplies them to the received vector, checking if the result is a validcodeword. Ordered reliability bits GRAND (ORBGRAND) improves on GRAND byconsidering soft information received from the channel. Both GRAND and ORBGRANDhave been implemented in hardware, focusing on average performance, sacrificingworst case throughput and latency. In this work, an improved pattern schedulefor ORBGRAND is proposed. It provides $>0.5$ dB gain over the standard scheduleat a block error rate $\le 10^{-5}$, and outperforms more complex GRAND flavorswith a fraction of the complexity. The proposed schedule is used within a novelcode-agnositic decoder architecture: the decoder guarantees fixed highthroughput and low latency, making it attractive for latency-constrainedapplications. It outperforms the worst-case performance of decoders by ordersof magnitude, and outperforms many best-case figures. Decoding a code of length128, it achieves a throughput of $79.21$ Gb/s with $58.49$ ns latency, yieldingbetter energy efficiency and comparable area efficiency with respect to thestate of the art.

A fixed latency ORBGRAND decoder architecture with LUT-aided error-pattern scheduling