Deep neural network (DNN) has attracted resurgent interest because of its great potential in implementing artificial intelligence (AI). One example for the excellent computing power of DNNs is the historic victories of AlphaGo over the best Go players in the world. However, the power consumption of the hardware behind AlphaGo is extremely high (150 kW level) as compared to that of a human brain (~25 W), not to mention that the former has much heavier weight and much bigger size. The race actually reveals the low energy-efficiency of using traditional CMOS devices to implement unconventional computing paradigms. To address the energy efficiency, size and weight issues, we believe computing systems built upon novel devices and nanotechnology, for instance memristors, offer an attractive solution for future computing. Using the conductance state to represent synaptic weight, non-volatile memristors do not need external power to maintain the states. The programming (weight updating) needs minute amount of energy as the device can be extremely small (<2 nm) and the switching speed can be very fast (<1ns). Organized into large arrays or stacked into 3D, memristors are capable of parallel in-memory computing using physical laws, such as Ohm's law and Kirchhoff's current law to implement vector matrix multiplication (VMM). Bringing computing into the memristor array, the new approach avoids the time and energy needed to access system memory in a digital system (so called 'von Neumann bottleneck'). Furthermore, the physical computing is analog in nature and the network could interface with analog data acquired directly from sensors, reducing the energy overhead from analog-to-digital conversion.
Our goal is to build an energy-efficient hardware platform using emerging electronic devices. Currently our lab is focused on the following main topics: 1)Energy-efficient hardware systems for machine intelligence, security, sensing and communication; 2) Emerging nanoelectronic devices: design, characterization and understanding; and 3) Enabling fabrication and three-dimensional heterogeneous integration technologies. Please see our publications from our recent projects.

Facilities and Equipment

We are well-equipped with nanofabrication, electrical measurement and physical characterization facilities for our research. Some of them are owned by our group, some are shared facilities on campus that we have full access to. A list of most relevant equipment is on the next page.


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