An asynchronous spike event coding scheme for programmable analog arrays

This paper presents a spike event coding scheme for the communication of analog signals in programmable analog arrays. In the scheme presented here no events are transmitted when the signals are constant leading to low power dissipation and traffic reduction in analog arrays. The design process and the implementation of the scheme in a programmable array context are explained. The validation of the presented scheme is performed using a speech signal. Finally, we demonstrate how the event coded scheme can perform summation of analog signals without additional hardware

Combined smart chemFET/resistive sensor array

Here we describe a novel CMOS compatible gas sensor array based on a combined resistive/chemFET sensor cell. We have fabricated an array of 70 sensors with integrated drive, gain and baseline removal circuitry using an AMS 0.6 mum CMOS process. The sensing materials are carbon black/polymer composite (CB) thin films, which have been previously reported to have good vapour-sensing properties. Different CB films have been deposited onto the sensor array and have been shown to respond differently to volatile organic compounds. This combined sensing element both reduces silicon area and, more importantly, measures different physical properties of the same gas sensitive material improving discrimination and giving more insight into the sensing mechanism

Spike-based analog-digital neuromorphic information processing system for sensor applications

A spiking-neuron-based system that combines analog and digital multi-processor implementations for the bio-inspired processing of sensors is reported. This combination allows creating a powerful bio-inspired multiple-input sensor processing system for environment perception applications. The analog front-end encodes the input signal in a signed spike representation, which is further processed by means of a digital Spiking Neural Network (SNN) on a Single-Instruction Multiple-Data (SIMD) multiprocessor. The spike distribution for both systems is based on Address-Event Representation (AER) scheme, asynchronous for the Analog Pre-Processor (APP) and synchronous for the Digital Multi-Processor (DMP), synchronized by means of an AER transceiver. A proof-of-concept application of the system being able to process sensory information has been demonstrated. The system utilizes 30-neurons emulated by the DMP to process spike-encoded information provided by its analog counterpart, enabling the feature extraction of the input signal. The frequency detection capability of the system is experimentally reported

Reduced-complexity equalization for EDGE

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN064194 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Spike-based analog-digital neuromorphic information processing system for sensor applications

A spiking-neuron-based system that combines analog and digital multi-processor implementations for the bio-inspired processing of sensors is reported. This combination allows creating a powerful bio-inspired multiple-input sensor processing system for environment perception applications. The analog front-end encodes the input signal in a signed spike representation, which is further processed by means of a digital Spiking Neural Network (SNN) on a Single-Instruction Multiple-Data (SIMD) multiprocessor. The spike distribution for both systems is based on Address-Event Representation (AER) scheme, asynchronous for the Analog Pre-Processor (APP) and synchronous for the Digital Multi-Processor (DMP), synchronized by means of an AER transceiver. A proof-of-concept application of the system being able to process sensory information has been demonstrated. The system utilizes 30-neurons emulated by the DMP to process spike-encoded information provided by its analog counterpart, enabling the feature extraction of the input signal. The frequency detection capability of the system is experimentally reported.Peer Reviewe