7 citations · 8 across the 8 of their papers we have counts for
8 papers
TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning
Avik Bhatnagar, Federico Nicolas Peccia, Oliver Bringmann
The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, where deep learning models are…
Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programs
Federico Nicolas Peccia, Frederik Haxel, Oliver Bringmann
RISC-V provides a flexible and scalable platform for applications ranging from embedded devices to high-performance computing clusters. Particularly, its RISC-V Vector Extension (R…
Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators
Konstantin Lübeck, Alexander Louis-Ferdinand Jung, Felix Wedlich +8
Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a challenging task that requires tailored hardware accelerator architectures and a clear understand…
HAPM -- Hardware Aware Pruning Method for CNN hardware accelerators in resource constrained devices
Federico Nicolas Peccia, Luciano Ferreyro, Alejandro Furfaro
During the last years, algorithms known as Convolutional Neural Networks (CNNs) had become increasingly popular, expanding its application range to several areas. In particular, th…
Efficient Edge AI: Deploying Convolutional Neural Networks on FPGA with the Gemmini Accelerator
Federico Nicolas Peccia, Svetlana Pavlitska, Tobias Fleck +1
The growing concerns regarding energy consumption and privacy have prompted the development of AI solutions deployable on the edge, circumventing the substantial CO2 emissions asso…
Embedded Distributed Inference of Deep Neural Networks: A Systematic Review
Federico Nicolás Peccia, Oliver Bringmann
Embedded distributed inference of Neural Networks has emerged as a promising approach for deploying machine-learning models on resource-constrained devices in an efficient and scal…