Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Neuro-inspired system for real-time vision sensor tilt correction

Neuromorphic engineering tries to mimic biological information processing. Address-Event-Representation (AER) is an asynchronous protocol for transferring the information of spiking neuro-inspired systems. Currently AER systems are able sense visual and auditory stimulus, to process information, to learn, to control robots, etc. In this paper we present an AER based layer able to correct in real time the tilt of an AER vision sensor, using a high speed algorithmic mapping layer. A codesign platform (the AER-Robot platform), with a Xilinx Spartan 3 FPGA and an 8051 USB microcontroller, has been used to implement the system. Testing it with the help of the USBAERmini2 board and the jAER software.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-02Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Neuromorphic electronics for real-time biomedical image processing

Published versio

AER and dynamic systems co-simulation over Simulink with Xilinx System Generator

Address-Event Representation (AER) is a neuromorphic communication protocol for transferring information of spiking neurons implemented into VLSI chips. These neuro-inspired implementations have been used to design sensor chips (retina, cochleas), processing chips (convolutions, filters) and learning chips, what makes possible the development of complex, multilayer, multichip neuromorphic systems. In biology one of the last steps of the processing is to move a muscle, to apply the results of these complex neuromorphic processing to the real world. One interesting question is to be able to transform, or translate, the AER information into robot movements, like for example, moving a DC motor. This paper presents several ways to translate AER spikes into DC motor power, and to control a DC motor speed, based on Pulse Frequency Modulation. These methods have been simulated into Simulink with Xilinx System Generator, and tested into the AER-Robot platform.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0

A review of current neuromorphic approaches for vision, auditory, and olfactory sensors

Conventional vision, auditory, and olfactory sensors generate large volumes of redundant data and as a result tend to consume excessive power. To address these shortcomings, neuromorphic sensors have been developed. These sensors mimic the neuro-biological architecture of sensory organs using aVLSI (analog Very Large Scale Integration) and generate asynchronous spiking output that represents sensing information in ways that are similar to neural signals. This allows for much lower power consumption due to an ability to extract useful sensory information from sparse captured data. The foundation for research in neuromorphic sensors was laid more than two decades ago, but recent developments in understanding of biological sensing and advanced electronics, have stimulated research on sophisticated neuromorphic sensors that provide numerous advantages over conventional sensors. In this paper, we review the current state-of-the-art in neuromorphic implementation of vision, auditory, and olfactory sensors and identify key contributions across these fields. Bringing together these key contributions we suggest a future research direction for further development of the neuromorphic sensing field