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.

翻译：机器学习算法的高效及时计算对于自动驾驶、物联网和边缘计算等新兴技术至关重要。在此类系统中，卷积神经网络是主要的机器学习算法之一，需要大量的计算资源。这一需求促使人们使用GPGPU等机器学习加速器来满足设计约束。然而，选择最合适的加速器需要进行设计空间探索，这一过程通常耗时且需要大量人工操作。本研究提出了加速设计空间探索的方法，为CNN推理系统识别最合适的GPGPU。我们开发了一种快速且精确的技术，用于预测CNN推理过程中的功耗和性能，其平均绝对百分比误差（MAPE）分别为5.03%和5.94%。该方法使计算机架构师能够在开发早期阶段估算功耗和性能，从而减少对大量原型的依赖。这不仅节省了时间和成本，还缩短了产品上市周期。