6 papers
AxMoE: Characterizing the Impact of Approximate Multipliers on Mixture-of-Experts DNN Architectures
Omkar B Shende, Marcello Traiola, Gayathri Ananthanarayanan
Deep neural network (DNN) inference at the edge demands simultaneous improvements in accuracy, computational efficiency, and energy consumption. Approximate computing and Mixture-o…
KANtize: Exploring Low-bit Quantization of Kolmogorov-Arnold Networks for Efficient Inference
Sohaib Errabii, Olivier Sentieys, Marcello Traiola
Kolmogorov-Arnold Networks (KANs) have gained attention for their potential to outperform Multi-Layer Perceptrons (MLPs) in terms of parameter efficiency and interpretability. Unli…
ENFOR-SA: End-to-end Cross-layer Transient Fault Injector for Efficient and Accurate DNN Reliability Assessment on Systolic Arrays
Rafael Billig Tonetto, Marcello Traiola, Fernando Fernandes dos Santos +1
Recent advances in deep learning have produced highly accurate but increasingly large and complex DNNs, making traditional fault-injection techniques impractical. Accurate fault an…
Domain-specific Hardware Acceleration for Model Predictive Path Integral Control
Erwan Tanguy-Legac, Tommaso Belvedere, Gianluca Corsini +2
Accurately controlling a robotic system in real time is a challenging problem. To address this, the robotics community has adopted various algorithms, such as Model Predictive Cont…
KAN-SAs: Efficient Acceleration of Kolmogorov-Arnold Networks on Systolic Arrays
Sohaib Errabii, Olivier Sentieys, Marcello Traiola
Kolmogorov-Arnold Networks (KANs) have garnered significant attention for their promise of improved parameter efficiency and explainability compared to traditional Deep Neural Netw…
SWAPPER: Dynamic Operand Swapping in Non-commutative Approximate Circuits for Online Error Reduction
Marcello Traiola, Nazar Misyats, Silviu-Ioan Filip +2
Error-tolerant applications, such as multimedia processing, machine learning, signal processing, and scientific computing, can produce satisfactory outputs even when approximate co…