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Graphics processor units (GPUs) offer high performance and power efficiency for a large number of data-parallel applications. Previous research has shown that a GPU-based version of a neuromorphic motion estimation algorithm can achieve a ×32 speedup using these devices. However, the memory consumption creates a bottleneck due to the expansive tree of signal processing operations performed. In the present contribution, an improvement in memory reduction was carried out, which limiteddoi:10.1186/1687-6180-2013-23 fatcat:bftgdch3gnayhpzjuq7cmgoxdm