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Found 7 papers, 0 papers with code
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning
Privacy-Preserving ML (PPML) based on Homomorphic Encryption (HE) is a promising foundational privacy technology.
Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training
At constant model quality, MLET allows embedding dimension, and model size, reduction by up to 16x, and 5. 8x on average, across the models.
Mixed-Precision Quantization with Cross-Layer Dependencies
Quantization assigning the same bit-width to all layers leads to large accuracy degradation at low precision and is wasteful at high precision settings.
A Provably Secure Strong PUF based on LWE: Construction and Implementation
We construct lattice PUF with a physically obfuscated key and an LWE decryption function block.
Variability-Aware Training and Self-Tuning of Highly Quantized DNNs for Analog PIM
DNNs deployed on analog processing in memory (PIM) architectures are subject to fabrication-time variability.
Power-Based Attacks on Spatial DNN Accelerators
Spatial accelerators, that parallelize matrix/vector operations, are utilized for enhancing energy efficiency of DNN computation.
Training with Multi-Layer Embeddings for Model Reduction
Increasing the dimension of embedding vectors improves model accuracy but comes at a high cost to model size.
