Crossbar-aligned & integer-only neural network compression for efficient in-memory acceleration

• المؤلفون الرئيسيون: Huai, Shuo, Liu, Di, Luo, Xiangzhong, Chen, Hui, Liu, Weichen, Subramaniam, Ravi
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Computer science and engineering::Computing methodologies; In-Memory Computing; Pruning; Quantization; Neural Networks
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/165352
• المؤسسة: Nanyang Technological University

Crossbar-based In-Memory Computing (IMC) accelerators preload the entire Deep Neural Network (DNN) into crossbars before inference. However, devices with limited crossbars cannot infer increasingly complex models. IMC-pruning can reduce the usage of crossbars, but current methods need expensive extr...