5 research outputs found
Real-time Neuromorphic Visual Pre-Processing and Dynamic Saliency
The human brain is by far the most computationally complex, efficient, and reliable computing system operating under such low-power, small-size, and light-weight specifications. Within the field of neuromorphic engineering, we seek to design systems with facsimiles to that of the human brain with means to reach its desirable properties. In this doctoral work, the focus is within the realm of vision, specifically visual saliency and related visual tasks with bio-inspired, real-time processing. The human visual system, from the retina through the visual cortical hierarchy, is responsible for extracting visual information and processing this information, forming our visual perception. This visual information is transmitted through these various layers of the visual system via spikes (or action potentials), representing information in the temporal domain. The objective is to exploit this neurological communication protocol and functionality within the systems we design. This approach is essential for the advancement of autonomous, mobile agents (i.e. drones/MAVs, cars) which must perform visual tasks under size and power constraints in which traditional CPU or GPU implementations to not suffice. Although the high-level objective is to design a complete visual processor with direct physical and functional correlates to the human visual system, we focus on three specific tasks.
The first focus of this thesis is the integration of motion into a biologically-plausible proto-object-based visual saliency model. Laurent Itti, one of the pioneers in the field, defines visual saliency as ``the distinct subjective perceptual quality which makes some items in the world stand out from their neighbors and immediately grab our attention.'' From humans to insects, visual saliency is important for the extraction of only interesting regions of visual stimuli for further processing. Prior to this doctoral work, Russel et al. \cite{russell2014model} designed a model of proto-object-based visual saliency with biological correlates. This model was designed for computing saliency only on static images. However, motion is a naturally occurring phenomena that plays an essential role in both human and animal visual processing. Henceforth, the most ideal model of visual saliency should consider motion that may be exhibited within the visual scene. In this work a novel dynamic proto-object-based visual saliency is described which extends the Russel et. al. saliency model to consider not only static, but also temporal information. This model was validated by using metrics for determining how accurate the model is in predicting human eye fixations and saccades on a public dataset of videos with attached eye tracking data. This model outperformed other state-of-the-art visual saliency models in computing dynamic visual saliency. Such a model that can accurately predict where humans look, can serve as a front-end component to other visual processors performing tasks such as object detection and recognition, or object tracking. In doing so it can reduce throughput and increase processing speed for such tasks. Furthermore, it has more obvious applications in artificial intelligence in mimicking the functionality of the human visual system.
The second focus of this thesis is the implementation of this visual saliency model on an FPGA (Field Programmable Gate Array) for real-time processing. Initially, this model was designed within MATLAB, a software-based approach running on a CPU, which limits the processing speed and consumes unnecessary amounts of power due to overhead. This is detrimental for integration with an autonomous, mobile system which must operate in real-time. This novel FPGA implementation allows for a low-power, high-speed approach to computing visual saliency. There are a few existing FPGA-based implementations of visual saliency, and of those, none are based on the notion of proto-objects. This work presents the first, to our knowledge, FPGA implementation of an object-based visual saliency model. Such an FPGA implementation allows for the low-power, light-weight, and small-size specifications that we seek within the field of neuromorphic engineering. For validating the FPGA model, the same metrics are used for determining the extent to which it predicts human eye saccades and fixations. We compare this hardware implementation to the software model for validation.
The third focus of this thesis is the design of a generic neuromorphic platform both on FPGA and VLSI (Very-Large-Scale-Integration) technology for performing visual tasks, including those necessary in the computation of the visual saliency. Visual processing tasks such as image filtering and image dewarping are demonstrated via this novel neuromorphic technology consisting of an array of hardware-based generalized integrate-and-fire neurons. It allows the visual saliency model's computation to be offloaded onto this hardware-based architecture. We first demonstrate an emulation of this neuromorphic system on FPGA demonstrating its capability of dewarping and filtering tasks as well as integration with a neuromorphic camera called the ATIS (Asynchronous Time-based Image Sensor). We then demonstrate the neuromorphic platform implemented in CMOS technology, specifically designed for low-mismatch, high-density, and low-power. Such a VLSI technology-based platform further bridges the gap between engineering and biology and moves us closer towards developing a complete neuromorphic visual processor
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Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain.
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 principal 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. This article focuses on the discussion of large-scale emulators and is a continuation of a previous review of various neural and synapse circuits (Indiveri et al., 2011). We also explore applications where these emulators have been used and discuss some of their promising future applications
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Corrigendum: Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain (vol 12, 891, 2018)
Thakur CS, Molin JL, Cauwenberghs G, et al. Corrigendum: Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain (vol 12, 891, 2018). FRONTIERS IN NEUROSCIENCE. 2019;12: 991