In this paper, we present a dynamic load-balancing algorithm for parallel digital logic simulation making use of reinforcement learning. We first introduce two dynamic load-balancing algorithms oriented towards balancing the computational and communication load respectively and then utilize reinforcement learning to create an algorithm which is a combination of the first two algorithms. In addition, the algorithm determines the value of two important parameters-the number of processors which participate in the algorithm and the load which is exchanged during its execution. We investigate the algorithms on gate level simulations of several open source VLSI circuits.

As proscribed by Moore's law, the size of integrated circuits has grown geometrically, resulting in simulation becoming the major bottleneck in the circuit design process. Parallel simulation provides us with a way to cope with this growth. In this paper, we describe an optimistic (time warp) parallel discrete event simulator which can simulate all synthesizeable Verilog circuits. We investigate its scalability and describe a machine learning based dynamic load balancing algorithm for use with the simulator. We initially developed two dynamic load balancing algorithms to balance the load and the communication, respectively, during the course of a simulation. Making use of reinforcement learning (RL), we then created an algorithm which is an amalgam of these two algorithms. To the best of our knowledge, this is the first time that RL has been used for the dynamic load-balancing of time warp. We investigated the scalability and the effectiveness of the dynamic load balancing algorithms on gate level simulations of several realistic very large scale integration (VLSI) circuits. Our experimental results showed that our simulator is indeed scalable. They also reveled a 88.6% improvement in the simulation time through the use of our RL algorithm.

As a consequence of Moore's law, the size of integrated circuits has grown extensively, resulting in simulation becoming the major bottleneck in the circuit design process. Consequently, parallel simulation has emerged as an approach which can be both fast and cost effective. In this paper, we examine the performance of a parallel Verilog simulator on four large, real designs. As previous work has made use of either relatively small benchmarks or synthetic circuits, the use of these circuits is far more realistic. We develop a parser for Verilog files enabling us to simulate in parallel all synthesizable Verilog circuits. We utilize four circuits as our test benches; the LEON Processor with 200 k gates, the OpenSparc T2 processor with 400 k gates and two Viterbi decoder circuits with 100 k and 800 k gates respectively. The simulator makes use of XTW and to our knowledge is the first Verilog simulator which can parse all synthesizable Verilog files. We observed 4,000,000 events per second on 32 processors for the Viterbi decoder with 800 k gates. The simulators' performance was shown to be scalable.