Graph neural networks: A powerful and versatile tool for advancing design, reliability, and security of ICs
Graph neural networks (GNNs) have pushed the state-of-the-art (SOTA) for performance in
learning and predicting on large-scale data present in social networks, biology, etc. Since …
learning and predicting on large-scale data present in social networks, biology, etc. Since …
Gamora: Graph learning based symbolic reasoning for large-scale boolean networks
Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-
level netlists can significantly benefit functional verification, logic minimization, datapath …
level netlists can significantly benefit functional verification, logic minimization, datapath …
: Backdoor Attack on Graph Neural Networks-Based Hardware Security Systems
Graph neural networks (GNNs) have shown great success in detecting intellectual property
(IP) piracy and hardware Trojans (HTs). However, the machine learning community has …
(IP) piracy and hardware Trojans (HTs). However, the machine learning community has …
TrojanSAINT: Gate-level netlist sampling-based inductive learning for hardware Trojan detection
H Lashen, L Alrahis, J Knechtel… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
We propose TrojanSAINT, a graph neural network (GNN)-based hardware Trojan (HT)
detection scheme working at the gate level. Unlike prior GNN-based art, TrojanSAINT …
detection scheme working at the gate level. Unlike prior GNN-based art, TrojanSAINT …
HDCircuit: Brain-inspired hyperdimensional computing for circuit recognition
Circuits possess a non-Euclidean representation, necessitating the encoding of their data
structure (eg, gate-level netlists) into fixed formats like vectors. This work is the first to …
structure (eg, gate-level netlists) into fixed formats like vectors. This work is the first to …
Verilog-to-PyG-A Framework for Graph Learning and Augmentation on RTL Designs
The complexity of modern hardware designs necessitates advanced methodologies for
optimizing and analyzing modern digital systems. In recent times, machine learning (ML) …
optimizing and analyzing modern digital systems. In recent times, machine learning (ML) …
Graph neural networks for hardware vulnerability analysis—can you trust your GNN?
L Alrahis, O Sinanoglu - 2023 IEEE 41st VLSI Test Symposium …, 2023 - ieeexplore.ieee.org
The participation of third-party entities in the globalized semiconductor supply chain
introduces potential security vulnerabilities, such as intellectual property piracy and …
introduces potential security vulnerabilities, such as intellectual property piracy and …
CircuitHD: Brain-inspired Hyperdimensional Computing for Circuit Recognition
Circuits possess an irregular representation. Hence, their data structure (eg, gate-level
netlists) necessitate the encoding into fixed formats like vectors. For machine learning-based …
netlists) necessitate the encoding into fixed formats like vectors. For machine learning-based …
MaliGNNoma: GNN-Based Malicious Circuit Classifier for Secure Cloud FPGAs
The security of cloud field-programmable gate arrays (FPGAs) faces challenges from
untrusted users attempting fault and side-channel attacks through malicious circuit …
untrusted users attempting fault and side-channel attacks through malicious circuit …
Design-space exploration of multiplier approximation in cnns
SN Raghava, HC Prashanth, BG Gowda… - 2023 IEEE Computer …, 2023 - ieeexplore.ieee.org
In terms of the hardware design, approximation offers not only low power and compact
footprint benefits but also accelerated outcomes. Quantization of the data format is one of the …
footprint benefits but also accelerated outcomes. Quantization of the data format is one of the …