The grand challenge of neuromorphic computation is to develop a flexible brain-like architecture capable of a wide array of real-time applications, while striving towards the ultra-low power consumption and compact size of the human brain---within the constraints of existing silicon technologies. In my talk, I describe two projects that I am involved in that make progress towards this ultimate goal. First, I describe Stanford’s Neurogrid project, a sixteen-chip desktop supercomputer for neuroscientists that models a million spiking neurons in real time. Second, I describe current research in IBM’s Cognitive Computing group, and in particular our work on the SyNAPSE project to build brain-like cognitive computing chips by moving beyond the von Neumann architecture.