SecureLoop: Design Space Exploration of Secure DNN Accelerators
Deep neural networks (DNNs) are gaining popularity in a wide range of domains, ranging
from speech and video recognition to healthcare. With this increased adoption comes the …
from speech and video recognition to healthcare. With this increased adoption comes the …
Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real System
The recent huge advance of Large Language Models (LLMs) is mainly driven by the
increase in the number of parameters. This has led to substantial memory capacity …
increase in the number of parameters. This has led to substantial memory capacity …
Memory-Centric Computing: Recent Advances in Processing-in-DRAM
Memory-centric computing aims to enable computation capability in and near all places
where data is generated and stored. As such, it can greatly reduce the large negative …
where data is generated and stored. As such, it can greatly reduce the large negative …
Amplifying Main Memory-Based Timing Covert and Side Channels using Processing-in-Memory Operations
The adoption of processing-in-memory (PiM) architectures has been gaining momentum
because they provide high performance and low energy consumption by alleviating the data …
because they provide high performance and low energy consumption by alleviating the data …
Gpu-based private information retrieval for on-device machine learning inference
On-device machine learning (ML) inference can enable the use of private user data on user
devices without revealing them to remote servers. However, a pure on-device solution to …
devices without revealing them to remote servers. However, a pure on-device solution to …
Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases
Today's computing systems require moving data back-and-forth between computing
resources (eg, CPUs, GPUs, accelerators) and off-chip main memory so that computation …
resources (eg, CPUs, GPUs, accelerators) and off-chip main memory so that computation …
Accelerating Confidential Recommendation Model Inference with Near-Memory Processing
Trusted Executing Environments (TEEs) in hardware designs protect program execution
from other untrusted software programs in the processor as well as untrusted off-chip …
from other untrusted software programs in the processor as well as untrusted off-chip …
Systematic Use of Random Self-Reducibility against Physical Attacks
This work presents a novel, black-box software-based countermeasure against physical
attacks including power side-channel and fault-injection attacks. The approach uses the …
attacks including power side-channel and fault-injection attacks. The approach uses the …
SoK: A Systems Perspective on Compound AI Threats and Countermeasures
Large language models (LLMs) used across enterprises often use proprietary models and
operate on sensitive inputs and data. The wide range of attack vectors identified in prior …
operate on sensitive inputs and data. The wide range of attack vectors identified in prior …
Next-Generation Datacenter Infrastructure for Large-Scale Personalized Recommendation
L Ke - 2023 - search.proquest.com
Today's personalized recommendation systems leverage deep learning to deliver the best
user experience in internet services used by search engines, social networks, online retail …
user experience in internet services used by search engines, social networks, online retail …