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Browsing by Author "Taheri, Ebadollah, author"

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    SCRIPT: a multi-objective routing framework for securing chiplet systems against distributed DoS attacks
    (Colorado State University. Libraries, 2024-06-12) Taheri, Ebadollah, author; Aghanoury, Pooya, author; Pasricha, Sudeep, author; Nikdast, Mahdi, author; Sehatbakhsh, Nader, author; ACM, publisher
    Heterogeneous 2.5D integration enables seamless integration of chiplets, hence reducing design time and costs. Concerns arise when dealing with untrustworthy chiplets, emphasizing the need for dependable Network-on-Interposer (NoI). This paper introduces SCRIPT, a secure routing framework to mitigate Distributed Denial-of-Service (DDoS) attacks in chiplet systems. SCRIPT obscures predictable paths exploited by attackers, disrupting orchestrated attacks. SCRIPT considers chiplet trust and criticality and employs a multi-objective optimization technique to enhance NoI performance and reliability. Evaluations show that SCRIPT enhances NoI security by at least 64% against DDoS attacks.
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    TRINE: a tree-based silicon photonic interposer network for energy-efficient 2.5D machine learning acceleration
    (Colorado State University. Libraries, 2023-10-28) Taheri, Ebadollah, author; Mahdian, Mohammad Amin, author; Pasricha, Sudeep, author; Nikdast, Mahdi, author; ACM, publisher
    2.5D chiplet systems have showcased low manufacturing costs and modular designs for machine learning (ML) acceleration. Nevertheless, communication challenges arise from chiplet interconnectivity and high-bandwidth demands among chiplets. To address these challenges, we present TRINE, a novel tree-based silicon photonic interposer network for energy-efficient ML acceleration. Leveraging silicon photonics and broadband optical switching, TRINE enables efficient inter-chiplet communication with reduced latency and improved energy efficiency. Considering several ML workloads, our simulation results demonstrate significant improvements in the average energy efficiency by 61.7% and 40% when comparing TRINE with two recently proposed silicon photonic interposer networks. By overcoming communication limitations in 2.5D ML accelerators, this work is a promising step towards advancing 2.5D photonic-based ML accelerator design.