Digit-Serial DA-Based Fixed-Point RNNs: A Unified Approach for Enhancing Architectural Efficiency.

The next crucial step in artificial intelligence involves integrating neural network models into embedded and mobile systems. This requires designing compact and energy-efficient neural network models in silicon for optimized performance. This article introduces a unified approach for enhancing the architectural efficiency of long short-term memory (LSTM) recurrent neural networks (RNNs).

Sustainable Resource Allocation and Reduce Latency Based on Federated-Learning-Enabled Digital Twin in IoT Devices.

In this article, we utilize Digital Twins (DT) with edge networks using blockchain technology for reliable real-time data processing and provide a secure, scalable solution to bridge the gap between physical edge networks and digital systems. Then, we suggest a Federated Learning (FL) framework for collaborative computing that runs on a blockchain and is powered by the DT edge network. This framework increases data privacy while enhancing system security and reliability.