Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, congestion, and timing) are fixed during pretraining. However, fixed-weighed models cannot generate the diversity of placements required for engineers to accommodate changing requirements as they arise. This paper proposes flexible multiple-objective reinforcement learning (MORL) to support objective functions with inference-time variable weights using just a single pretrained model. Our macro placement results show that MORL can generate the Pareto frontier of multiple objectives effectively.

Resistive Random Access Memory (ReRAM) Crossbars are a promising process-in-memory technology to reduce enormous data movement overheads of large-scale graph processing between computation and memory units. ReRAM cells can combine with crossbar arrays to effectively accelerate graph processing, and partitioning ReRAM crossbar arrays into Operation Units (OUs) can further improve computation accuracy of ReRAM crossbars. The operation unit utilization was not optimized in previous work, incurring extra cost. This paper proposes a two-stage algorithm with a crossbar OU-aware scheme for sparse graph index remapping for ReRAM (SGIRR) crossbars, mitigating the influence of graph sparsity. In particular, this paper is the first to consider the given operation unit size with the remapping index algorithm, optimizing the operation unit and power dissipation. Experimental results show that our proposed algorithm reduces the utilization of crossbar OUs by 31.4%, improves the total OU block usage by 10.6%, and saves energy consumption by 17.2%, on average.