Unity Human Eye Model for Gaze Tracking with a Query-Driven Dynamic Vision Sensor.

Objective measurement of gaze pattern and eye movement during untethered activity has important applications for neuroscience research and neurological disease detection. Current commercial eye-tracking tools rely on desk-top devices with infrared emitters and conventional frame-based cameras. Although wearable options do exist, the large power-consumption from their conventional cameras limit true long-term mobile usage. The query-driven Dynamic Vision Sensor (qDVS) is a neuromorphic camera which dramatically reduces power consumption by outputting only intensity-change threshold events, as opposed to full frames of intensity data. However, such hardware has not yet been implemented for on-body eye-tracking, but the feasibility can be demonstrated using a mathematical simulator to evaluate the eye-tracking ca-pabilities of the qDVS under controlled conditions. Specifically, a framework utilizing a realistic human eye model in the 3D graphics engine, Unity, is presented to enable the controlled and direct comparison of image-based gaze tracking methods. Eye-tracking based on qDVS frames was compared against two different conventional frame eye-tracking methods - the traditional ellipse pupil-fitting algorithm and a deep learning neural network inference model. Gaze accuracy from qDVS frames achieved an average of 93.2% for movement along the primary horizontal axis (pitch angle) and 93.1 % for movement along the primary vertical axis (yaw angle) under 4 different illumination conditions, demonstrating the feasibility for using qDVS hardware cameras for such applications. The quantitative framework for the direct comparison of eye tracking algorithms presented here is made open-source and can be extended to include other eye parameters, such as pupil dilation, reflection, motion artifact, and more.

Low-Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing.

Brain-inspired neuromorphic computing has the potential to revolutionize the current computing paradigm with its massive parallelism and potentially low power consumption. However, the existing approaches of using digital complementary metal-oxide-semiconductor devices (with "0" and "1" states) to emulate gradual/analog behaviors in the neural network are energy intensive and unsustainable; furthermore, emerging memristor devices still face challenges such as nonlinearities and large write noise. Here, an electrochemical graphene synapse, where the electrical conductance of graphene is reversibly modulated by the concentration of Li ions between the layers of graphene is presented. This fundamentally different mechanism allows to achieve a good energy efficiency (<500 fJ per switching event), analog tunability (>250 nonvolatile states), good endurance, and retention performances, and a linear and symmetric resistance response. Essential neuronal functions such as excitatory and inhibitory synapses, long-term potentiation and depression, and spike timing dependent plasticity with good repeatability are demonstrated. The scaling study suggests that this simple, two-dimensional synapse is scalable in terms of switching energy and speed.