My research focuses on designing and developing new hardware systems for Deep Learning that offer orders-of-magnitude performance/power benefits over current approaches. This includes: i) novel non-Von Neumann architectures based on Non-Volatile Memory that can accomplish highly parallelized Multiply-Accumulate operations at the location of data, and ii) FPGA-based systems that leverage approximate computing methods including reduced precision and aggressive compression schemes.