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DTSTART;TZID=America/Los_Angeles:20260302T130000
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DTSTAMP:20260305T195727Z
CREATED:20260305T195727Z
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UID:16729-1772456400-1772460000@www.cecs.uci.edu
SUMMARY:A Pareto-Optimal Compiler for Mapping Task Graphs to Truly Scalable Grids of Processing Cells
DESCRIPTION:Name: Yutong Wang \nChair: Prof. Rainer Doemer \nDate: March 2\, 2026 \nTime: 1:00 PM \nLocation: EH 3206 \nCommittee: Prof. Brian Demsky\, Prof. Zhou Li \nTitle: A Pareto-Optimal Compiler for Mapping Task Graphs to Truly Scalable Grids of Processing Cells \nAbstract: Programming scalable system-on-chip architectures composed of processor arrays remains challenging due to the complexity of partitioning applications\, assigning tasks and data\, and realizing communication under strict locality constraints. Regular grid-based architectures offer scalability and predictable communication\, yet mapping real applications onto such platforms is often manual\, error-prone\, and difficult to validate. This dissertation presents the Grid of Processing Cells Compiler (GPCC)\, an automated framework that maps application task graphs extracted from C++ specifications onto a truly scalable two-dimensional grid of processing cells with local memories and neighbor-only communication. GPCC automatically analyzes application graphs\, performs task and data partitioning\, places tasks onto grid locations\, and generates consistent on-chip and off-chip communication\, producing executable SystemC TLM-2.0 architecture models for system-level simulation and evaluation. GPCC employs a two-level mapping strategy in which tasks are first placed onto processing cells using a dependency-aware recursive search\, followed by assignment of communication channels and external I/O resources. In addition\, this work introduces an automated design space exploration method that evaluates candidate grid dimensions and identifies Pareto-optimal configurations. Experimental results on embedded applications and synthetic benchmarks demonstrate that GPCC efficiently produces correct executable models\, enables systematic exploration of grid-based architectures\, and determines suitable grid dimensions with minimal manual effort. \nBio: Yutong Wang is a Ph.D. candidate in Computer Engineering at UC Irvine\, working in scalable system-on-chip architectures and SystemC-based modeling. His research centers on automated mapping and design space exploration for grid-based processor arrays through the Grid of Processing Cells Compiler (GPCC).
URL:https://www.cecs.uci.edu/event/a-pareto-optimal-compiler-for-mapping-task-graphs-to-truly-scalable-grids-of-processing-cells/
LOCATION:EH 3206\, Engineering Hall University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/webp:https://cecs.uci.edu/files/2026/03/yutong.webp
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DTSTART;TZID=America/Los_Angeles:20260518T130000
DTEND;TZID=America/Los_Angeles:20260518T140000
DTSTAMP:20260824T233918Z
CREATED:20260824T233717Z
LAST-MODIFIED:20260824T233918Z
UID:16893-1779109200-1779112800@www.cecs.uci.edu
SUMMARY:Securing Cyber-Physical Systems: From Sensor-Layer Attacks to Firmware Binary Analysis
DESCRIPTION:Name: Yonatan Gizachew Achamyeleh \nChair: Mohammad Al Faruque\nMember: Zhou Li\nMember: Yasser Shoukry \nDate: 05/18/2026\nTime: 01:00 PM PT\nLocation: ISEB 1010 \nTitle: Securing Cyber-Physical Systems: From Sensor-Layer Attacks to Firmware Binary Analysis\nAbstract: This dissertation advances security across the full stack of cyber-physical systems\, from the physical sensor layer to the firmware binary layer. Four contributions demonstrate that sensors are a pervasive and underprotected attack surface\, showing that pressure sensors can be exploited to eavesdrop on nearby audio\, acoustic signals can destabilize UAV flight\, physical sensor corruption misaligns Vision-Language Model-driven robots\, and sensor rate of change can trigger hardware Trojans covertly. Three further contributions develop tools for semantic recovery from stripped industrial firmware\, spanning binary embeddings\, function name prediction\, and LLM-guided decompilation\, achieving over 94% recompilability across six architectures. The unifying argument is that the sensor and binary layers are structurally coupled\, and that securing one requires understanding the other. \nBio: Yonatan Gizachew Achamyeleh is a PhD candidate in Computer Engineering at the University of California\, Irvine\, advised by Professor Mohammad Abdullah Al Faruque. His research spans cyber-physical systems security\, embedded binary analysis\, and the intersection of physical-layer attacks with AI-driven autonomy.
URL:https://www.cecs.uci.edu/event/securing-cyber-physical-systems-from-sensor-layer-attacks-to-firmware-binary-analysis/
LOCATION:CA
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/png:https://cecs.uci.edu/files/2026/08/Yonatan-Achamyeleh.png
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DTSTART;TZID=America/Los_Angeles:20260608T100000
DTEND;TZID=America/Los_Angeles:20260608T110000
DTSTAMP:20260716T025310Z
CREATED:20260716T025012Z
LAST-MODIFIED:20260716T025310Z
UID:16853-1780912800-1780916400@www.cecs.uci.edu
SUMMARY:Adaptive Sparsity for Efficient and Reliable Machine Learning Inference in Autonomous Vehicles
DESCRIPTION:Name: Danny Abraham \nChair: Prof. Nikil Dutt \nDate: June 8\, 2026 \nTime: 10:00 AM \nLocation: DBH 4011 \nCommittee: Prof. Fadi Kurdahi\, Prof. Mohsen Imani \nTitle: Adaptive Sparsity for Efficient and Reliable Machine Learning Inference in Autonomous Vehicles \nAbstract: Autonomous vehicles rely on machine learning models that must make accurate decisions under strict latency\, memory\, and reliability constraints. However\, modern deep learning systems are often designed as fixed\, dense inference pipelines\, even though real driving conditions vary across compute availability\, scene difficulty\, sensor structure\, and modality access.\nThis dissertation presents adaptive sparsity as a framework for making autonomous vehicle inference more efficient and responsive to these changing conditions. It explores three complementary forms of sparsity: parameter sparsity for dynamically adjusting model capacity\, activation sparsity for exploiting input-dependent computation\, and input sparsity for using rich training-time modalities to improve deployment-time models with fewer inputs. Together\, these methods show how sparsity can serve not only as a compression tool\, but as a mechanism for adaptive\, efficient\, and reliable inference in autonomous vehicle systems. \nBio: Danny Abraham is a PhD student in the School of Computer Science at UC Irvine\, working with Dr. Nikil Dutt\, in the field of embedded machine learning. His current line of work is researching adaptive machine learning models for resource-constrained embedded systems\, focusing on dynamic runtime reconfiguration. This enables real-time model adjustment and optimizes the tradeoff between accuracy\, latency\, and energy consumption particularly in the context of autonomous vehicles.
URL:https://www.cecs.uci.edu/event/adaptive-sparsity-for-efficient-and-reliable-machine-learning-inference-in-autonomous-vehicles/
LOCATION:DBH 4011\, Donald Bren Hall\, University of California\, Irvine\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
ATTACH;FMTTYPE=image/jpeg:https://cecs.uci.edu/files/2026/07/danny.jpg
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