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DNN Memory Footprint Reduction via Post-Training Intra-Layer Multi-Precision Quantization
The imperative to deploy Deep Neural Network (DNN) models on resource-constrained edge devices, spurred by privacy concerns, has become increasingly apparent.
FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions
Deep neural networks (DNNs) are increasingly being deployed in safety-critical systems such as personal healthcare devices and self-driving cars.
BDFA: A Blind Data Adversarial Bit-flip Attack on Deep Neural Networks
Adversarial bit-flip attack (BFA) on Neural Network weights can result in catastrophic accuracy degradation by flipping a very small number of bits.
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