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Found 18 papers, 8 papers with code
Entropy-Guided Attention for Private LLMs
The pervasiveness of proprietary language models has raised critical privacy concerns, necessitating advancements in private inference (PI), where computations are performed directly on encrypted data without revealing users' sensitive information.
TruncFormer: Private LLM Inference Using Only Truncations
Private inference (PI) serves an important role in guaranteeing the privacy of user data when interfacing with proprietary machine learning models such as LLMs.
AERO: Softmax-Only LLMs for Efficient Private Inference
The pervasiveness of proprietary language models has raised privacy concerns for users' sensitive data, emphasizing the need for private inference (PI), where inference is performed directly on encrypted inputs.
ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models
LayerNorm is a critical component in modern large language models (LLMs) for stabilizing training and ensuring smooth optimization.
PriViT: Vision Transformers for Fast Private Inference
The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications.
Towards Fast and Scalable Private Inference
The characterization motivates the need for both GCs and HE accelerators.
Selective Network Linearization for Efficient Private Inference
To reduce PI latency we propose a gradient-based algorithm that selectively linearizes ReLUs while maintaining prediction accuracy.
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
In this work, we ask: Is it feasible to substitute all ReLUs with low-degree polynomial activation functions for building deep, privacy-friendly neural networks?
Sphynx: ReLU-Efficient Network Design for Private Inference
The emergence of deep learning has been accompanied by privacy concerns surrounding users' data and service providers' models.
Circa: Stochastic ReLUs for Private Deep Learning
In this paper we re-think the ReLU computation and propose optimizations for PI tailored to properties of neural networks.
Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors
This poses a potential risk to user privacy.
DeepReDuce: ReLU Reduction for Fast Private Inference
This paper proposes DeepReDuce: a set of optimizations for the judicious removal of ReLUs to reduce private inference latency.
CryptoNAS: Private Inference on a ReLU Budget
Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to process inferences without disclosing inputs.
DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure.
Distributed, Parallel, and Cluster Computing
MASR: A Modular Accelerator for Sparse RNNs
The architecture is enhanced by a series of dynamic activation optimizations that enable compact storage, ensure no energy is wasted computing null operations, and maintain high MAC utilization for highly parallel accelerator designs.
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
The widespread application of deep learning has changed the landscape of computation in the data center.
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
This results in up to a 1. 51x improvement over the state-of-the-art.
Fathom: Reference Workloads for Modern Deep Learning Methods
Fathom has been released online, and this paper focuses on understanding the fundamental performance characteristics of each model.
