Publications

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The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.

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As transistors start to approach fundamental limits and Moore's law slows down, new devices and architectures are needed to enable continued performance gains. New approaches based on RRAM (resistive random access memory) or memristor crossbars can enable the processing of large amounts of data[1, 2]. One of the most promising applications for RRAM crossbars is brain inspired or neuromorphic computing[3, 4].

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Tunneling Field Effect Transistors (TFETs) have the potential to achieve a low operating voltage by overcoming the thermally limited subthreshold swing of 60mV/decade, but results to date have been unsatisfying. Unfortunately, TFETs have only shown steep subthreshold swings at low currents of a nA/μm or lower while we would like a mA/μm. To understand this we need to consider the two switching mechanisms in a TFET. The gate voltage can be used to modulate the tunneling barrier thickness and thus the tunneling probability as shown Fig. 1(a). Alternatively, it is possible use energy filtering or density of states (DOS) switching as illustrated in Fig. 1(b). If the conduction and valence band don't overlap, no current can flow. Once they do overlap, current can flow.

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