iMAD: an in-memory accelerator for AdderNet with efficient 8-bit addition and subtraction operations

• Main Authors: Zhu, Shien, Li, Shiqing, Liu, Weichen
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering::Computer systems organization::Special-purpose and application-based systems; Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; In-Memory Computing; Convolutional Neural Network; AdderNet
• Online Access: https://hdl.handle.net/10356/156404
• Institution: Nanyang Technological University

Adder Neural Network (AdderNet) is a new type of Convolutional Neural Networks (CNNs) that replaces the computational-intensive multiplications in convolution layers with lightweight additions and subtractions. As a result, AdderNet preserves high accuracy with adder convolution kernels and achieves...