3 citations · 3 across the 4 of their papers we have counts for
4 papers
FeatureBleed: Inferring Private Enriched Attributes From Sparsity-Optimized AI Accelerators
Darsh Asher, Farshad Dizani, Joshua Kalyanapu +3
Backend enrichment is now widely deployed in sensitive domains such as product recommendation pipelines, healthcare, and finance, where models are trained on confidential data and…
GATEBLEED: Exploiting On-Core Accelerator Power Gating for High Performance & Stealthy Attacks on AI
Joshua Kalyanapu, Farshad Dizani, Darsh Asher +4
As power consumption from AI training and inference continues to increase, AI accelerators are being integrated directly into the CPU. Intel's Advanced Matrix Extensions (AMX) is o…
Decode and Forward Relaying for SC-FDE Systems with a Multi-Antenna Relay
Farshad Dizani, Ali Olfat
In this paper, a cooperative relay network consisting of a single-antenna source, a multi-antenna relay, and a multi-antenna destination is considered. The relay operates in decode…
THOR: A Non-Speculative Value Dependent Timing Side Channel Attack Exploiting Intel AMX
Farshad Dizani, Azam Ghanbari, Joshua Kalyanapu +2
The rise of on-chip accelerators signifies a major shift in computing, driven by the growing demands of artificial intelligence (AI) and specialized applications. These accelerator…