No abstract

No abstract

No abstract

In this paper, we will present Shark, a software based logic verification technology that allows high-performance switch-level simulation of multi-million transistor circuits on general-purpose workstations. Shark achieves high-performance simulations on very large circuits through three key technologies: 1) a circuit partitioner based on latch boundary components, design hierarchy driven clustering, and latch/activity load balancing, 2) a high-performance switch-level simulator capable of simulating very large models and run word-parallel simulations, and 3) a simulation backplane that can connect any number of simulators to form a distributed/parallel simulation environment. Shark has been tested on circuits of up to 15M transistors. On an Intel circuit with about 5M transistors, Shark achieved a simulation throughput of 19Hz.

Connection management is an important problem for any wireless network to ensure smooth and well-balanced operation throughout. Traditional methods for connection management (specifically user-cell association) consider sub-optimal and greedy solutions such as connection of each user to a cell with maximum receive power. However, network performance can be improved by leveraging machine learning (ML) and artificial intelligence (AI) based solutions. The next generation software defined 5G networks defined by the Open Radio Access Network (O-RAN) alliance facilitates the inclusion of ML/AI based solutions for various network problems. In this paper, we consider intelligent connection management based on the O-RAN network architecture to optimize user association and load balancing in the network. We formulate connection management as a combinatorial graph optimization problem. We propose a deep reinforcement learning (DRL) solution that uses the underlying graph to learn the weights of the graph neural networks (GNN) for optimal user-cell association. We consider three candidate objective functions: sum user throughput, cell coverage, and load balancing. Our results show up to 10% gain in throughput, 45-140% gain cell coverage, 20-45% gain in load balancing depending on network deployment configurations compared to baseline greedy techniques.