A silicon implementation of the fly's optomotor control system

Flies are capable of stabilizing their body during free flight by using visual motion information to estimate self-rotation. We have built a hardware model of this optomotor control system in a standard CMOS VLSI process. The result is a small, low-power chip that receives input directly from the real world through on-board photoreceptors and generates motor commands in real time. The chip was tested under closed-loop conditions typically used for insect studies. The silicon system exhibited stable control sufficiently analogous to the biological system to allow for quantitative comparisons

Neuromorphic analogue VLSI

Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do

CMOS VLSI correlator design for radio-astronomical signal processing : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Auckland, New Zealand

Multi-element radio telescopes employ methods of indirect imaging to capture the image of the sky. These methods are in contrast to direct imaging methods whereby the image is constructed from sensor measurements directly and involve extensive signal processing on antenna signals. The Square Kilometre Array, or the SKA, is a future radio telescope of this type that, once built, will become the largest telescope in the world. The unprecedented scale of the SKA requires novel solutions to be developed for its signal processing pipeline one of the most resource-consuming parts of which is the correlator. The SKA uses the FX correlator construction that consists of two parts: the F part that translates antenna signals into frequency domain and the X part that cross-correlates these signals between each other. This research focuses on the integrated circuit design and VLSI implementation issues of the X part of a very large FX correlator in 28 nm and 130 nm CMOS. The correlator’s main processing operation is the complex multiply-accumulation (CMAC) for which custom 28 nm CMAC designs are presented and evaluated. Performance of various memories inside the correlator also affects overall efficiency, and input-buffered and output-buffered approaches are considered with the goal of improving upon it. For output-buffered designs, custom memory control circuits have been designed and prototyped in 130 nm that improve upon eDRAM by taking advantage of sequential access patterns. For the input-buffered architecture, a new scheme is proposed that decreases the usage of the input-buffer memory by a third by making use of multiple accumulators in every CMAC. Because cross-correlation is a very data-intensive process, high-performance SerDes I/O is essential to any practical ASIC implementation. On the I/O design, the 28 nm full-rate transmitter delivering 15 Gbps per lane is presented. This design consists of the scrambler, the serialiser, the digital VCO with analog fine-tuning and the SST driver including features of a 4-tap FFE, impedance tuning and amplitude tuning

A Binaural Neuromorphic Auditory Sensor for FPGA: A Spike Signal Processing Approach

This paper presents a new architecture, design flow, and field-programmable gate array (FPGA) implementation analysis of a neuromorphic binaural auditory sensor, designed completely in the spike domain. Unlike digital cochleae that decompose audio signals using classical digital signal processing techniques, the model presented in this paper processes information directly encoded as spikes using pulse frequency modulation and provides a set of frequency-decomposed audio information using an address-event representation interface. In this case, a systematic approach to design led to a generic process for building, tuning, and implementing audio frequency decomposers with different features, facilitating synthesis with custom features. This allows researchers to implement their own parameterized neuromorphic auditory systems in a low-cost FPGA in order to study the audio processing and learning activity that takes place in the brain. In this paper, we present a 64-channel binaural neuromorphic auditory system implemented in a Virtex-5 FPGA using a commercial development board. The system was excited with a diverse set of audio signals in order to analyze its response and characterize its features. The neuromorphic auditory system response times and frequencies are reported. The experimental results of the proposed system implementation with 64-channel stereo are: a frequency range between 9.6 Hz and 14.6 kHz (adjustable), a maximum output event rate of 2.19 Mevents/s, a power consumption of 29.7 mW, the slices requirements of 11 141, and a system clock frequency of 27 MHz.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130

From Vision Sensor to Actuators, Spike Based Robot Control through Address-Event-Representation

One field of the neuroscience is the neuroinformatic whose aim is to develop auto-reconfigurable systems that mimic the human body and brain. In this paper we present a neuro-inspired spike based mobile robot. From commercial cheap vision sensors converted into spike information, through spike filtering for object recognition, to spike based motor control models. A two wheel mobile robot powered by DC motors can be autonomously controlled to follow a line drown in the floor. This spike system has been developed around the well-known Address-Event-Representation mechanism to communicate the different neuro-inspired layers of the system. RTC lab has developed all the components presented in this work, from the vision sensor, to the robot platform and the FPGA based platforms for AER processing.Ministerio de Ciencia e Innovación TEC2006-11730-C03-02Junta de Andalucía P06-TIC-0141