New advances in artificial neural networks and machine learning techniques

Peer ReviewedPostprint (published version

Optimized Implementation of Neuromorphic HATS Algorithm on FPGA

In this paper, we present first-ever optimized hardware implementation of a state-of-the-art neuromorphic approach Histogram of Averaged Time Surfaces (HATS) algorithm to event-based object classification in FPGA for asynchronous time-based image sensors (ATIS). Our Implementation achieves latency of 3.3 ms for the N-CARS dataset samples and is capable of processing 2.94 Mevts/s. Speed-up is achieved by using parallelism in the design and multiple Processing Elements can be added. As development platform, Zynq-7000 SoC from Xilinx is used. The tradeoff between Average Absolute Error and Resource Utilization for fixed precision implementation is analyzed and presented. The proposed FPGA implementation is $\sim$ 32 x power efficient compared to software implementation

An Optoelectronic Stimulator for Retinal Prosthesis

Retinal prostheses require the presence of viable population of cells in the inner retina. Evaluations of retina with Age-Related Macular Degeneration (AMD) and Retinitis Pigmentosa (RP) have shown a large number of cells remain in the inner retina compared with the outer retina. Therefore, vision loss caused by AMD and RP is potentially treatable with retinal prostheses. Photostimulation based retinal prostheses have shown many advantages compared with retinal implants. In contrary to electrode based stimulation, light does not require mechanical contact. Therefore, the system can be completely external and not does have the power and degradation problems of implanted devices. In addition, the stimulating point is flexible and does not require a prior decision on the stimulation location. Furthermore, a beam of light can be projected on tissue with both temporal and spatial precision. This thesis aims at fi nding a feasible solution to such a system. Firstly, a prototype of an optoelectronic stimulator was proposed and implemented by using the Xilinx Virtex-4 FPGA evaluation board. The platform was used to demonstrate the possibility of photostimulation of the photosensitized neurons. Meanwhile, with the aim of developing a portable retinal prosthesis, a system on chip (SoC) architecture was proposed and a wide tuning range sinusoidal voltage-controlled oscillator (VCO) which is the pivotal component of the system was designed. The VCO is based on a new designed Complementary Metal Oxide Semiconductor (CMOS) Operational Transconductance Ampli er (OTA) which achieves a good linearity over a wide tuning range. Both the OTA and the VCO were fabricated in the AMS 0.35 µm CMOS process. Finally a 9X9 CMOS image sensor with spiking pixels was designed. Each pixel acts as an independent oscillator whose frequency is controlled by the incident light intensity. The sensor was fabricated in the AMS 0.35 µm CMOS Opto Process. Experimental validation and measured results are provided

Chalcogenide and metal-oxide memristive devices for advanced neuromorphic computing

Energy-intensive artificial intelligence (AI) is prevailing and changing the world, which requires energy-efficient computing technology. However, traditional AI driven by von Neumann computing systems suffers from the penalties of high-energy consumption and time delay due to frequent data shuttling. To tackle the issue, brain-inspired neuromorphic computing that performs data processing in memory is developed, reducing energy consumption and processing time. Particularly, some advanced neuromorphic systems perceive environmental variations and internalize sensory signals for localized in-senor computing. This methodology can further improve data processing efficiency and develop multifunctional AI products. Memristive devices are one of the promising candidates for neuromorphic systems due to their non-volatility, small size, fast speed, low-energy consumption, etc. In this thesis, memristive devices based on chalcogenide and metal-oxide materials are fabricated for neuromorphic computing systems. Firstly, a versatile memristive device (Ag/CuInSe2/Mo) is demonstrated based on filamentary switching. Non-volatile and volatile features are coexistent, which play multiple roles of non-volatile memory, selectors, artificial neurons, and artificial synapses. The conductive filaments’ lifetime was controlled to present both volatile and non-volatile behaviours. Secondly, the sensing functions (temperature and humidity) are explored based on Ag conductive filaments. An intelligent matter (Ag/Cu(In, Ga)Se2/Mo) endowing reconfigurable temperature and humidity sensations is developed for sensory neuromorphic systems. The device reversibly switches between two states with differentiable semiconductive and metallic features, demonstrating different responses to temperature and humidity variations. Integrated devices can be employed for intelligent electronic skin and in-sensor computing. Thirdly, the memristive-based sensing function of light was investigated. An optoelectronic synapse (ITO/ZnO/MoO3/Mo) enabling multi-spectrum sensitivity for machine vision systems is developed. For the first time, this optoelectronic synapse is practical for front-end retinomorphic image sensing, convolution processing, and back-end neuromorphic computing. This thesis will benefit the development of advanced neuromorphic systems pushing forward AI technology

Benchmarking spike-based visual recognition: a dataset and evaluation

Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organisation have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarks and that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware implementations. With this dataset we hope to (1) promote meaningful comparison between algorithms in the field of neural computation, (2) allow comparison with conventional image recognition methods, (3) provide an assessment of the state of the art in spike-based visual recognition, and (4) help researchers identify future directions and advance the field