Efficient and timely calculations of Machine Learning (ML) algorithms are
essential for emerging technologies like autonomous driving, the Internet of
Things (IoT), and edge computing. One of the primary ML algorithms used in such
systems is Convolutional Neural Networks (CNNs), which demand high
computational resources. This requirement has led to the use of ML accelerators
like GPGPUs to meet design constraints. However, selecting the most suitable
accelerator involves Design Space Exploration (DSE), a process that is usually
time-consuming and requires significant manual effort. Our work presents
approaches to expedite the DSE process by identifying the most appropriate
GPGPU for CNN inferencing systems. We have developed a quick and precise
technique for forecasting the power and performance of CNNs during inference,
with a MAPE of 5.03% and 5.94%, respectively. Our approach empowers computer
architects to estimate power and performance in the early stages of
development, reducing the necessity for numerous prototypes. This saves time
and money while also improving the time-to-market period.

我们的工作通过识别最合适的 GPGPU 用于 CNN 推理系统，提出了一种加快 DSE 过程的方法。我们开发了一种快速而精确的技术来预测 CNN 推理过程中的功耗和性能，MAPE 分别为 5.03％和 5.94％。这种方法使计算机架构师能够在开发初期估计功耗和性能，减少了大量原型的必要性。这不仅节省了时间和金钱，同时也改善了上市时间。