Prof. Cawenberghs presents neuromorphic cognitive computing systems-on-chip implemented in custom silicon compute-in-memory neural and memristive synaptic crossbar array architectures that combine the efficiency of local interconnects with flexibility and sparsity in global interconnects, and that realize a wide class of deeply layered and recurrent neural network topologies with embedded local plasticity for on-line learning, at a fraction of the computational and energy cost of implementation on CPU and GPGPU platforms.  Co-optimization across the abstraction layers of hardware and algorithms leverage inherent stochasticity in the physics of synaptic memory devices and neural interface circuits with plasticity in reconfigurable massively parallel architecture towards high system-level accuracy, resilience, and efficiency.  Adiabatic energy recycling in charge-mode crossbar arrays permit extreme scaling in energy efficiency, approaching that of synaptic transmission in the mammalian brain.