The word 'accelerated' does a lot of quiet work, so here's the plain version: training a model means repeatedly showing it data and nudging its parameters. Accelerating that can mean many things, fewer steps to converge, cheaper steps, or skipping work you've already done. US20210390414A1 (published December 16, 2021) describes one such method.
The CPC tags, G06N 3/084 (backpropagation/learning) and G06N 3/0454 (network architecture) plus G06N 5/046 (inference rules), point to optimization of the learning process itself. The patent's framing is the universal goal: same model, same accuracy, less wall-clock time and less compute to get there.
“Apparatuses, systems, and techniques to accelerate training of one or more neural networks.”— U.S. Patent Application 2021/0390414 A1 source
Under the hood, the usual accelerants fall into a few buckets. You can change the math (mixed-precision arithmetic, better optimizers), change the data flow (curriculum or smarter batching), or change the starting point (warm-starting from a related model so you don't learn from scratch). Each shaves time off the expensive part.
Why a general reader should care: training cost is the upstream driver of the whole AI economics debate. Every method that makes training cheaper changes what's affordable to build. The capex headlines are downstream of thousands of small accelerations like this one, compounding across the industry.
The honest gloss: 'accelerated' in a patent title is a claim about a method, not a measured speedup you can quote, and a publication isn't a grant. What it documents is that by the end of 2021, making training faster was a dense enough field that incremental methods were routinely worth filing, a sign of a maturing, cost-conscious discipline.