  Computational storage, known as a solution to significantly reduce the
latency by moving data-processing down to the data storage, has received wide
attention because of its potential to accelerate data-driven devices at the
edge. To meet the insatiable appetite for complicated functionalities tailored
for intelligent devices such as autonomous vehicles, properties including
heterogeneity, scalability, and flexibility are becoming increasingly
important. Based on our prior work on hierarchical erasure coding that enables
scalability and flexibility in cloud storage, we develop an efficient decoding
algorithm that corrects a mixture of errors and erasures simultaneously. We
first extract the basic component code, the so-called extended Cauchy (EC)
codes, of the proposed coding solution. We prove that the class of EC codes is
strictly larger than that of relevant codes with known explicit decoding
algorithms. Motivated by this finding, we then develop an efficient decoding
method for the general class of EC codes, based on which we propose the local
and global decoding algorithms for the hierarchical codes. Our proposed hybrid
error correction not only enables the usage of hierarchical codes in
computational storage at the edge, but also applies to any Cauchy-like codes
and allows potentially wider applications of the EC codes.
