This paper presents a comprehensive investigation into the deployment of customized digital hardware for accelerating AI inference on edge devices. It evaluates the performance trade-offs among ASICs,
Neural Network Architecture Search focuses on designing novel DNN architectures tailored for mobile and IoT devices, enhancing computation and storage efficiency on edge devices.
Mixed-Precision Quantization (MPQ) has become a key technique for deploying deep neural networks on resource-constrained IoT edge devices, enabling efficient TinyML applications while maintaining
Because of the above-mentioned issues of cloud computing, a fast and efficient DNN model running on edge devices is necessary. There are three major techniques for achieving on
The Light ODN solution accurately plans the ODN network. With the pre-connectorized products, light-weight construction of ODN, splicing-free, and rapid construction.
Building upon these findings, we developed a framework that proposes two algorithms: one for discovering optimal pruning and the second for determining the optimal number of clusters.
This paper presents an optimization triad for efficient and reliable edge AI deployment, including data, model, and system optimization. First, we discuss optimizing data through data cleaning,
Recent efforts at quantizing DNNs have employed a range of techniques en-compassing progressive quantization, step-size adaptation, and gradient scaling. This paper proposes a new quanti-zation
We analyze the trade-offs between latency, energy, and accuracy across various techniques, highlighting practical deployment strategies on real-world devices.
Contact us today for product inquiries, custom kits, or integration support