This paper presents an approach that permits the effective hardware realization of a novel Evolvable Spiking Neural Network (ESNN) paradigm on Field Programmable Gate Arrays (FPGAs). The ESNN possesses a hybrid learning algorithm that consists of a Spike Timing Dependent Plasticity (STDP) mechanism fused with a Genetic Algorithm (GA). The design and implementation direction utilizes the latest advancements in FPGA technology to provide a partitioned hardware/software co-design solution. The approach achieves the maximum FPGA flexibility obtainable for the ESNN paradigm. The algorithm was applied as an embedded intelligent system robotic controller to solve an autonomous navigation and obstacle avoidance problem.

The Australia SKA Pathfinder (ASKAP) is a new telescope under development as a world-class high-dynamic-range wide-field-of-view survey instrument. It will utilize focal plane phased array feeds on the 36 12-m antennas that will compose the array. The large amounts of data present a huge computing challenge, and ASKAP will store data products in an archive after near real-time pipeline processing. This powerful instrument will be deployed at a new radio-quiet observatory, the Murchison Radio-astronomy Observatory in the midwest region of Western Australia, to enable sensitive surveys of the entire sky to address some of the big questions in contemporary physics. As a pathfinder for the SKA, ASKAP will demonstrate field of view enhancement and computing/processing technology as well as the operation of a large-scale radio array in a remote and radio-quiet region of Australia.