The intensity-to-time processing paradigm implements global operations by aggregating only a few of the input data at a time. Inspired by biological vision, the paradigm is based on the notion that stronger input signals elicit responses before weaker inputs do. Assuming that the inputs have different intensities, the intensity-to-time paradigm separates responses in time allowing a global processor to makes decisions based only on a few inputs at a time. The more time allowed, the more responses are received; thus, the global processor incrementally builds a global decision first based on several, and eventually based on all, the inputs. The key is that some preliminary decision about the environment can be made as soon as the first responses are received. Therefore, this paradigm has an important place in low-latency vision processing. The architecture supporting intensity-to-time processing is shown in Fig. 1. After a common reset signal at t=0, a cell generates an event at the instant t_k = f(I_k) where f(.) is a monotonic function, and I_k the radiation received by the cell k. Therefore, any two cells receiving radiation of different magnitude generate events at different times. If f(.) is decaying, then the ordering of events is consistent with a biological system: stronger stimuli elicit responses before weaker ones.

A global processor receives and processes events. In addition, there can be a local processor attached to each cell. The generated events then control the instant when a local processor in each cell performs at least one predetermined (i.e., prewired or preprogrammed) operation. By separating the input data in time, the intensity-to-time processing paradigm eases the global data aggregation and computation:

1. global processor processes only a few events at a time;
2. communication resources are shared by many cells;
3. global processor and local processors infer the input operand intensity by measuring the time an event is received [e.g., I_k=f ^-1(t_k)].

Traditionally, the intensity-to-time relationship has been used in single and double slope A/D converters. In vision, it has been used to improve diffusion-based image segmentation—a local operation, and for image acquisition in a SIMD architecture—an architecture well suited only for local operations. In contrast, our architecture allows global operations and shares some features of traditional MIMD parallel processing. Namely, the local processors perform their operations asynchronously, an essential feature for the quick response and the low latency performance of parallel systems.

The intensity-to-time is closely related to the event-address neural communication schemes proposed by researchers at Caltec. In these schemes the plurality of artificial neurons fire pulses (i.e., events) at rates reflecting their individual levels of activity. The goal is to communicate this activity to other neurons or to an output device. The event-address scheme shares communication wires by communicating the identity (i.e., address) of the neuron when it fires a pulse. Since the time is inherently measured across the entire system, the receiver recovers the firing rate for each transmitting neuron. The intensity-to-time paradigm synchronizes the responses at the beginning of operations and deals with the time intervals each “neuron” takes to fire its first pulse.

We used intensity to time paradigm to build Sorting Image Sensor and Blob Tracking Sensor.