This letter presents an efficient end-to-end workflow for deploying DNNs on an SoC/FPGA by integrating hyperparameter tuning through Bayesian optimization (BO) with an ensemble of …

By covering such diverse topics as DNN-to-accelerator toolflows, high-throughput cascaded classifiers and domain-specific model design, the presented set of works aim to enable the …

Nov 25, 2021 · In this post, I will talk about what are the approaches to optimizing your CNN to run on embedded system. In terms of deployment, there are two routes: cloud vs. edge …

Designing a power-efficient edge artificial intelligence (AI) system while achieving a faster time to market can become tedious in the absence of the right tools and software from an embedded …

In this work, we present OmniBoost, a framework that utilizes both performance and functional heterogeneity of embedded devices to increase system throughput via DNN partitioning.

Jul 8, 2020 · We propose a novel pipelined architecture for DNNs that avoids all useless operations during the inference process. It has been implemented in a field-programmable …

of existing CNN architectures for real-time inference on embedded systems. We show that this architecture, dubbed CondenseNeXt, is remarkably efficient in comparison to the baseline …

Dec 22, 2020 · Abstract: The spread of deep learning on embedded devices has prompted the development of numerous methods to optimize the deployment of deep neural networks …

Jan 24, 2024 · In this Work-in-Progress paper, we focus on the GAP9 RISC-V SoC as a case study to show how the open-source DORY Deep Neural Network (DNN) tool flow can be …

In order for machine learning to fulfill its promise in many industries, it is necessary to be able to deploy the inference (the part that executes the trained machine learning algorithm) into an …