Sequence alignment is a fundamental operation in a genomic analysis where DNA fragments called reads are mapped to a long reference DNA sequence. With the advent of next-generation sequencing (NGS) technologies that generate a mammoth amount of data, there is increased pressure on improving the performance and capacity of the analysis algorithms in general and the mapping algorithms in particular. Recently it has been demonstrated that restricting the mapping space for input reads and filtering out mapping positions that will result in a poor match can significantly improve the performance of the alignment operation. However, this is only true if it is guaranteed that the filtering operation can be performed significantly faster, otherwise it can easily outweigh the benefits of the aligner. The recently proposed GRIM-Filter uses highly-parallel processing-in-memory operations benefiting from light-weight computational units on the logic-in-memory layer. By analyzing input genomes, we found that there are unexpected data-reuse opportunities in the filtering operation. We propose an algorithm-hardware co-design that exploits the data-reuse in the seed location filtering operation and compared to the GRIM-Filter, cuts the number of memory accesses by 22-54%. This improves the overall performance and energy consumption by 19-44% and 21-49% respectively.