Machine Learning aided Computer Architecture Design for CNN Inferencing
  Systems
Christopher A. Metz
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.