activity
20182021
most citedSpecial Session: Reliability Analysis for ML/AI Hardware

4 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AR2021

Designing Efficient and High-performance AI Accelerators with Customized STT-MRAM

Kaniz Mishty, Mehdi Sadi

In this paper, we demonstrate the design of efficient and high-performance AI/Deep Learning accelerators with customized STT-MRAM and a reconfigurable core. Based on model-driven d…

cs.ET2021

True Random Number Generation using Latency Variations of Commercial MRAM Chips

Farah Ferdaus, B. M. S. Bahar Talukder, Mehdi Sadi +1

The emerging magneto-resistive RAM (MRAM) has considerable potential to become a universal memory technology because of its several advantages: unlimited endurance, lower read/writ…

cs.AR20214 cited

Special Session: Reliability Analysis for ML/AI Hardware

Shamik Kundu, Kanad Basu, Mehdi Sadi +4

Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many c…

cs.DC2020

Yield Loss Reduction and Test of AI and Deep Learning Accelerators

Mehdi Sadi, Ujjwal Guin

With data-driven analytics becoming mainstream, the global demand for dedicated AI and Deep Learning accelerator chips is soaring. These accelerators, designed with densely packed…

cs.CR2018

Hardware Trojan Detection through Information Flow Security Verification

Adib Nahiyan, Mehdi Sadi, Rahul Vittal +3

Semiconductor design houses are increasingly becoming dependent on third party vendors to procure intellectual property (IP) and meet time-to-market constraints. However, these thi…