  Modern deep neural networks must demonstrate state-of-the-art accuracy while
exhibiting low latency and energy consumption. As such, neural architecture
search (NAS) algorithms take these two constraints into account when generating
a new architecture. However, efficiency metrics such as latency are typically
hardware dependent requiring the NAS algorithm to either measure or predict the
architecture latency. Measuring the latency of every evaluated architecture
adds a significant amount of time to the NAS process. Here we propose
Microprocessor A Priori for Latency Estimation MAPLE that does not rely on
transfer learning or domain adaptation but instead generalizes to new hardware
by incorporating a prior hardware characteristics during training. MAPLE takes
advantage of a novel quantitative strategy to characterize the underlying
microprocessor by measuring relevant hardware performance metrics, yielding a
fine-grained and expressive hardware descriptor. Moreover, the proposed MAPLE
benefits from the tightly coupled I/O between the CPU and GPU and their
dependency to predict DNN latency on GPUs while measuring microprocessor
performance hardware counters from the CPU feeding the GPU hardware. Through
this quantitative strategy as the hardware descriptor, MAPLE can generalize to
new hardware via a few shot adaptation strategy where with as few as 3 samples
it exhibits a 6% improvement over state-of-the-art methods requiring as much as
10 samples. Experimental results showed that, increasing the few shot
adaptation samples to 10 improves the accuracy significantly over the
state-of-the-art methods by 12%. Furthermore, it was demonstrated that MAPLE
exhibiting 8-10% better accuracy, on average, compared to relevant baselines at
any number of adaptation samples.
