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A fully configurable bias current reference is described. The output of the current reference is a gate voltage which produces a desired current. For each daisy-chained bias, 32 bits of configuration are divided into 22 bits of bias current, 6 bits of active-mirror buffer current, and 4 bits of other configuration. Configuration of each bias allows specifying the type of transistor (nfet or pfet), whether the bias is enabled or weakly pulled to the rail, whether the bias is for a cascode, and whether the bias transistor uses a shifted source (SS) voltage for sub-off-current biasing. In addition, the current reference integrates a pair of voltage regulators that generate stable voltage sources near the rails, suitable for the SS current references. Measurements from fabricated current references built in 180 nm CMOS show that the reference achieves at least 110 dB (22-bit) dynamic range and reaches 160dB when power-rail gate biasing is included. Generated bias currents reach at least 30x smaller current than the transistor off-current. Each current reference occupies an area of 620 × 50 um2. The design kit schematics and layout are open-sourced.

This paper proposes a simple focal plane pattern detector architecture using a novel pixel sensor based on the dichromatic vertacolor structure. Additionally, the sensor transfers dichromatic intensity values using a self-timed time-to- first-spike scheme, which provides high dynamic range imaging. The intensity information is transmitted using the address event representation protocol. The spectral information is sampled automatically at each intensity reading in a ratioed way that maintains high dynamic range. A test chip consisting of 20 pixels has been fabricated in 1.5 um 2P 2M CMOS and characterized. The combined pattern detector/ imager core consumes 45 uA at 5 V supply voltage.

In this paper we describe an address-event vision system designed to detect accidental falls in elderly home care applications. The system raises an alarm when a fall hazard is detected. We use an asynchronous temporal contrast vision sensor which features sub-millisecond temporal resolution. A lightweight algorithm computes an instantaneous motion vector and reports fall events. We are able to distinguish fall events from normal human behavior, such as walking, crouching down, and sitting down. Our system is robust to the monitored person's spatial position in a room and presence of pets.

In this paper, we describe an address-event vision system designed to detect accidental falls in elderly home care applications. The system raises an alarm when a fall hazard is detected. We use an asynchronous temporal contrast vision sensor which features sub-millisecond temporal resolution. The sensor reports a fall at ten times higher temporal resolution than a frame-based camera and shows 84% higher bandwidth efficiency as it transmits fall events. A lightweight algorithm computes an instantaneous motion vector and reports fall events. We are able to distinguish fall events from normal human behavior, such as walking, crouching down, and sitting down. Our system is robust to the monitored person's spatial position in a room and presence of pets.

A 5-layer neuromorphic vision processor whose components communicate spike events asychronously using the address-eventrepresentation (AER) is demonstrated. The system includes a retina chip, two convolution chips, a 2D winner-take-all chip, a delay line chip, a learning classifier chip, and a set of PCBs for computer interfacing and address space remappings. The components use a mixture of analog and digital computation and will learn to classify trajectories of a moving object. A complete experimental setup and measurements results are shown.