Block Group Scheduling: A General Precision-scalable NPU Scheduling Technique with Capacity-aware Memory Allocation

Precision-scalable neural processing units (PSNPUs) efficiently provide native support for quantized neural networks. However, with the recent advancements of deep neural networks, PSNPUs are affected by a severe memory bottleneck owing to the need to perform an extreme number of simple computations simultaneously. In this study, we first analyze whether the memory bottleneck issue can be solved using conventional neural processing unit scheduling techniques. Subsequently, we introduce new capacity-aware memory allocation and block-level scheduling techniques to minimize the memory bottleneck. Compared with the baseline, the new method achieves up to 2.26x performance improvements by substantially relieving the memory pressure of low-precision computations without hardware overhead.