Twofold Sparsity: Joint Bit- and Network-Level Sparsity for Energy-Efficient Deep Neural Network Using RRAM Based Compute-In-Memory

Twofold Sparsity: Joint Bit- and Network-Level Sparsity for Energy-Efficient Deep Neural Network Using RRAM Based Compute-In-Memory

On-device intelligence and AI-powered edge devices require compressed deep learning algorithm and energy efficient hardware. Compute-in-memory (CIM) architecture is a more suitable candidate than traditional Complementary Metal-Oxide-Semiconductor (CMOS) technology for deep learning applications sin...

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Bibliographic Details
Main Authors: Foroozan Karimzadeh, Arijit Raychowdhury
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Computing-in-memory
deep learning compression
edge computing
quantization
sparsity
Online Access:https://ieeexplore.ieee.org/document/10459196/

Internet

https://ieeexplore.ieee.org/document/10459196/

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