The performance needs of memory systems caused by growing volumes of data from emerging applications, such as machine learning and big data analytics, have continued to increase. As a result, HBM has been introduced in GPUs and throughput oriented processors. HBM is a stack of multiple DRAM devices across a number of memory channels. Although HBM provides a large number of channels and high peak bandwidth, we observed that all channels are not evenly utilized and often only one or few channels are highly congested after applying the hashing technique to randomize the translated physical memory address. To solve this issue, we propose a cost-effective technique to improve load balancing for HBM channels. In the proposed memory system, a memory request from a busy channel can be migrated to other non-busy channels and serviced in the other channels. Moreover, this request migration reduces stalls by memory controllers, because the depth of a memory request queue in a memory controller is effectively increased by the migration. The improved load balancing of memory channels shows a 10.1% increase in performance for GPGPU workloads.