15 June 2021
Technology leadership claims can often be “economical” with the truth, and this is particularly true for NPUs (Neural Processor Units). However, over-stated performance can lead to procurement decisions that fall short of expectations. The result is engineering targets missed, deadlines not met, and R&D costs escalating. For its aiWare NPU, AImotive focuses on benchmarking NPU efficiency to provide the best information for AI engineers. Many other NPU suppliers quote raw TOPS and hardware utilization as a measure of performance. What’s the difference, and why does it matter?