Jamal Lottier Molin

Jamal Lottier Molin, Chetan Singh Thakur, Ralph Etienne-Cummings et al.

The ability to attend to salient regions of a visual scene is an innate and necessary preprocessing step for both biological and engineered systems performing high-level visual tasks (e.g. object detection, tracking, and classification). Computational efficiency, in regard to processing bandwidth and speed, is improved by only devoting computational resources to salient regions of the visual stimuli. In this paper, we first present a neuromorphic, bottom-up, dynamic visual saliency model based on the notion of proto-objects. This is achieved by incorporating the temporal characteristics of the visual stimulus into the model, similarly to the manner in which early stages of the human visual system extracts temporal information. This neuromorphic model outperforms state-of-the-art dynamic visual saliency models in predicting human eye fixations on a commonly used video dataset with associated eye tracking data. Secondly, for this model to have practical applications, it must be capable of performing its computations in real-time under low-power, small-size, and lightweight constraints. To address this, we introduce a Field-Programmable Gate Array implementation of the model on an Opal Kelly 7350 Kintex-7 board. This novel hardware implementation allows for processing of up to 23.35 frames per second running on a 100 MHz clock - better than 26x speedup from the software implementation.

Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs et al.

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principle advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers.