  Guessing Random Additive Noise Decoding (GRAND) is a universal decoding
algorithm that has been recently proposed as a practical way to perform maximum
likelihood decoding. It generates a sequence of possible error patterns and
applies them to the received vector, checking if the result is a valid
codeword. Ordered reliability bits GRAND (ORBGRAND) improves on GRAND by
considering soft information received from the channel. Both GRAND and ORBGRAND
have been implemented in hardware, focusing on average performance, sacrificing
worst case throughput and latency. In this work, an improved pattern schedule
for ORBGRAND is proposed. It provides $>0.5$ dB gain over the standard schedule
at a block error rate $\le 10^{-5}$, and outperforms more complex GRAND flavors
with a fraction of the complexity. The proposed schedule is used within a novel
code-agnositic decoder architecture: the decoder guarantees fixed high
throughput and low latency, making it attractive for latency-constrained
applications. It outperforms the worst-case performance of decoders by orders
of magnitude, and outperforms many best-case figures. Decoding a code of length
128, it achieves a throughput of $79.21$ Gb/s with $58.49$ ns latency, yielding
better energy efficiency and comparable area efficiency with respect to the
state of the art.
