The invention discloses a dynamically reconfigurable
machine learning acceleration
system, which ensures the structural
correctness of a
processing flow and no
data loss by checking the sequence exchange consistency and
queue / storage conservation in a running process in a cloud edge collaborative environment. The
system includes a plurality of seat modules, referred to as EDGE, NODE, CORE, FIN, and GOV, and a cross-seat exchange interconnect structure connecting these modules. During operation, the
system performs continuous consistency check on a key service link, and once an inconsistency or tearing fault is detected, the system only allows to adopt a single
repair action once, and selects repair measures according to a priority sequence of topology / path priority, secondary format / protocol and secondary
time sequence / window, so that the repair efficiency is improved. And it is ensured that one inconsistency can be eliminated only through one-time repair each time. The process converges in a unit stepping mode, and each iteration enables the inconsistent count mu to be reduced by 1 (
delta mu = 1) until the system reaches a consistent state of sealing.