9 citations · 42 across the 19 of their papers we have counts for
21 papers
Albireo: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge
Amir Taherin, José Cano, Bin Ren +2
Video object detection on edge devices runs computationally expensive detectors over long frame streams, causing high energy consumption and sustained GPU utilization. Although con…
PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs
Rappy Saha, Jude Haris, Nicolas Bohm Agostini +2
Power-of-two (PoT) quantization significantly reduces the size of deep neural networks (DNNs) and replaces multiplications with bit-shift operations for inference. Prior work has s…
GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
Lara D'Agata, Carlos Agulló-Domingo, Óscar Vera-López +7
Fully homomorphic encryption (FHE) has recently attracted significant attention as both a cryptographic primitive and a systems challenge. Given the latest advances in accelerated…
FHECore: Rethinking GPU Microarchitecture for Fully Homomorphic Encryption
Lohit Daksha, Seyda Guzelhan, Kaustubh Shivdikar +10
Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data but incurs massive computational and memory overheads, often exceeding plaintext execution by seve…
AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators
Nicolas Bohm Agostini, Jude Haris, Perry Gibson +6
This paper addresses the need for automatic and efficient generation of host driver code for arbitrary custom AXI-based accelerators targeting linear algebra algorithms, an importa…
FetaFix: Automatic Fault Localization and Repair of Deep Learning Model Conversions
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provid…