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Brain-computer interfaces (BCIs) enable direct communication with the brain, providing valuable information about brain function and enabling novel treatment of brain disorders. Our group has been building {\abssys}, a flexible and ultra-low-power processing architecture for BCIs. HALO can process up to 46Mbps of neural data, a significant increase over the interfacing bandwidth achievable by prior BCIs. HALO can also be programmed to support several applications, unlike most prior BCIs. Key to HALO's effectiveness is a hardware accelerator cluster, where each accelerator operates within its own clock domain. A configurable interconnect connects the accelerators to create data flow pipelines that realize neural signal processing algorithms. We have taped out our design in a 12nm CMOS process. The resulting chip runs at 0.88V, per-accelerator frequencies of 3--180MHz, and consumes at most 5.0mW for each signal processing pipeline. Evaluations using electrophysiological data collected from a non-human primate confirm HALO's flexibility and superior performance per watt. 

Multidimensional data is often visualized using coordinated multiple views in an interactive dashboard. However, unlike in infographics where text is often a central part of the presentation, there is currently little knowledge of how to best integrate text and annotations in a visualization dashboard. In this paper, we explore a technique called FacetNotes for presenting these textual annotations on top of any visualization within a dashboard irrespective of the scale of data shown or the design of visual representation itself. FacetNotes does so by grouping and ordering the textual annotations based on properties of (1) the individual data points associated with the annotations, and (2) the target visual representation on which they should be shown. We present this technique along with a set of user interface features and guidelines to apply it to visualization interfaces. We also demonstrate FacetNotes in a custom visual dashboard interface. Finally, results from a user study of FacetNotes show that the technique improves the scope and complexity of insights developed during visual exploration.

 

We propose topology-aware space distortion (TASD), a family of interactive layout techniques for non-linearly distorting geometric space based on user attention and on the structure of the visual representation. TASD seamlessly adapts the visual substrate of any visualization to give more screen real estate to important regions of the representation at the expense of less important regions. In this paper, we present a concrete TASD technique that we call ZoomHalo for interactively distorting a two-dimensional space based on a degree-of-interest (DOI) function defined for the space. Using this DOI function, ZoomHalo derives several areas of interest, computes the available space around each area in relation to other areas and the current viewport extents, and then dynamically expands (or shrinks) each area given user input. We use our prototype to evaluate the technique in two user studies, as well as showcase examples of TASD for node-link diagrams, word clouds, and geographical maps.