R
Ramrakhyani Prakash S
Researcher at University of Texas at Austin
Publications - 27
Citations - 372
Ramrakhyani Prakash S is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Cache & Memory address. The author has an hindex of 8, co-authored 27 publications receiving 257 citations. Previous affiliations of Ramrakhyani Prakash S include North Carolina State University & Association for Computing Machinery.
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Proceedings ArticleDOI
Morphable counters: enabling compact integrity trees for low-overhead secure memories
Gururaj Saileshwar,Prashant J. Nair,Ramrakhyani Prakash S,Wendy Arnott Elsasser,José A. Joao,Moinuddin K. Qureshi +5 more
TL;DR: This paper proposes a scalable solution to this problem by proposing a compact integrity tree design that requires fewer memory accesses for its traversal, and enables this by proposing new storage-efficient representations for the counters used for encryption and integrity-tree in secure memories.
Proceedings ArticleDOI
SYNERGY: Rethinking Secure-Memory Design for Error-Correcting Memories
Gururaj Saileshwar,Prashant J. Nair,Ramrakhyani Prakash S,Wendy Arnott Elsasser,Moinuddin K. Qureshi +4 more
TL;DR: This paper proposes Synergy, a reliability-security co-design that improves performance of secure execution while providing strong reliability for systems with 9-chip ECC-DIMMs and increases reliability by 185x compared to ECCs that provide Single-Error Correction, Double-Error Detection (SECDED) capability.
Proceedings ArticleDOI
A case for dynamic pipeline scaling
TL;DR: This paper makes the case that the useful frequency range of DVS is limited because there is a lower bound on voltage, and proposes Dynamic Pipeline Scaling (DPS), a DPS-enabled deep pipeline that has a deep mode for higher frequencies within the influ¿ence of D VS, and a shallow mode for lower frequencies.
Proceedings ArticleDOI
Shredder: Learning Noise Distributions to Protect Inference Privacy
Fatemehsadat Mireshghallah,Mohammadkazem Taram,Ramrakhyani Prakash S,Ali Jalali,Dean M. Tullsen,Hadi Esmaeilzadeh +5 more
TL;DR: Shredder as mentioned in this paper proposes an end-to-end framework that, without altering the topology or the weights of a pre-trained network, learns additive noise distributions that significantly reduce the information content of communicated data while maintaining the inference accuracy.
Posted Content
Shredder: Learning Noise Distributions to Protect Inference Privacy
Fatemehsadat Mireshghallah,Mohammadkazem Taram,Ramrakhyani Prakash S,Dean M. Tullsen,Hadi Esmaeilzadeh +4 more
TL;DR: Shredder is an end-to-end framework that, without altering the topology or the weights of a pre-trained network, learns additive noise distributions that significantly reduce the information content of communicated data while maintaining the inference accuracy.