ReaLPrune: ReRAM Crossbar-aware Lottery Ticket Pruned CNNs
Biresh Kumar Joardar, Janardhan Rao Doppa, Hai Li, Krishnendu, Chakrabarty, Partha Pratim Pande

TL;DR
ReaLPrune introduces a crossbar-aware pruning method for CNNs on ReRAM architectures, enabling over 90% weight pruning, significant hardware reduction, and faster training without accuracy loss, suitable for edge ML applications.
Contribution
The paper presents a novel crossbar-aware pruning strategy that significantly improves hardware efficiency and training speed for CNNs on ReRAM-based edge devices, outperforming existing methods.
Findings
Prunes over 90% of CNN weights without accuracy loss.
Reduces hardware requirements by 77.2%.
Speeds up CNN training by approximately 20 times.
Abstract
Training machine learning (ML) models at the edge (on-chip training on end user devices) can address many pressing challenges including data privacy/security, increase the accessibility of ML applications to different parts of the world by reducing the dependence on the communication fabric and the cloud infrastructure, and meet the real-time requirements of AR/VR applications. However, existing edge platforms do not have sufficient computing capabilities to support complex ML tasks such as training large CNNs. ReRAM-based architectures offer high-performance yet energy efficient computing platforms for on-chip CNN training/inferencing. However, ReRAM-based architectures are not scalable with the size of the CNN. Larger CNNs have more weights, which requires more ReRAM cells that cannot be integrated in a single chip. Moreover, training larger CNNs on-chip will require higher power,…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Memory and Neural Computing · Adversarial Robustness in Machine Learning
