TFix: Exploiting the Natural Redundancy of Ternary Neural Networks for Fault Tolerant In-Memory Vector Matrix Multiplication
Author:
Affiliation:
1. Purdue University,West Lafayette,Indiana
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10247654/10247655/10247835.pdf?arnumber=10247835
Reference27 articles.
1. Error Resilient In-Memory Computing Architecture for CNN Inference on the Edge
2. Improving Fault Tolerance for Reliable DNN using Boundary-Aware Activation
3. WESCO: Weight-encoded Reliability and Security Co-design for In-memory Computing Systems
4. Improving compute in-memory ECC reliability with successive correction
5. FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation
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1. BNN-Flip: Enhancing the Fault Tolerance and Security of Compute-in-Memory Enabled Binary Neural Network Accelerators;2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC);2024-01-22
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