Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey

Abstract

Deep Neural Networks (DNNs) are very popular because of their high performance in various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have brought beyond human accuracy in many tasks, but at the cost of high computational complexity. To enable efficient execution of DNN inference, more and more research works, therefore, exploit the inherent error resilience of DNNs and employ Approximate Computing (AC) principles to address the elevated energy demands of DNN accelerators. This article provides a comprehensive survey and analysis of hardware approximation techniques for DNN accelerators. First, we analyze the state of the art and by identifying approximation families, we cluster the respective works with respect to the approximation type. Next, we analyze the complexity of the performed evaluations (with respect to the dataset and DNN size) to assess the efficiency, the potential, and limitations of approximate DNN accelerators. Moreover, a broad discussion is provided, regarding error metrics that are more suitable for designing approximate units for DNN accelerators as well as accuracy recovery approaches that are tailored to DNN inference. Finally, we present how Approximate Computing for DNN accelerators can go beyond energy efficiency and address reliability and security issues, as well.

This paper has been accepted by ACM Computing Surveys (CSUR), 2022

References in corpus (10)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Distilling the Knowledge in a Neural Network
• Deep Learning with Limited Numerical Precision
• Trained Ternary Quantization
• Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead
• SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
• Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead
• AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers
• Discretization based Solutions for Secure Machine Learning against Adversarial Attacks
• Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference