41 research outputs found
Simulation and implementation of novel deep learning hardware architectures for resource constrained devices
Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems
Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals
Neuromodulation techniques have emerged as promising approaches for treating
a wide range of neurological disorders, precisely delivering electrical
stimulation to modulate abnormal neuronal activity. While leveraging the unique
capabilities of artificial intelligence (AI) holds immense potential for
responsive neurostimulation, it appears as an extremely challenging proposition
where real-time (low-latency) processing, low power consumption, and heat
constraints are limiting factors. The use of sophisticated AI-driven models for
personalized neurostimulation depends on back-telemetry of data to external
systems (e.g. cloud-based medical mesosystems and ecosystems). While this can
be a solution, integrating continuous learning within implantable
neuromodulation devices for several applications, such as seizure prediction in
epilepsy, is an open question. We believe neuromorphic architectures hold an
outstanding potential to open new avenues for sophisticated on-chip analysis of
neural signals and AI-driven personalized treatments. With more than three
orders of magnitude reduction in the total data required for data processing
and feature extraction, the high power- and memory-efficiency of neuromorphic
computing to hardware-firmware co-design can be considered as the
solution-in-the-making to resource-constraint implantable neuromodulation
systems. This could lead to a new breed of closed-loop responsive and
personalised feedback, which we describe as Neuromorphic Neuromodulation. This
can empower precise and adaptive modulation strategies by integrating
neuromorphic AI as tightly as possible to the site of the sensors and
stimulators. This paper presents a perspective on the potential of Neuromorphic
Neuromodulation, emphasizing its capacity to revolutionize implantable
brain-machine microsystems and significantly improve patient-specificity.Comment: 17 page
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Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices
Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s)
Review of medical data analysis based on spiking neural networks
Medical data mainly includes various types of biomedical signals and medical
images, which can be used by professional doctors to make judgments on
patients' health conditions. However, the interpretation of medical data
requires a lot of human cost and there may be misjudgments, so many scholars
use neural networks and deep learning to classify and study medical data, which
can improve the efficiency and accuracy of doctors and detect diseases early
for early diagnosis, etc. Therefore, it has a wide range of application
prospects. However, traditional neural networks have disadvantages such as high
energy consumption and high latency (slow computation speed). This paper
presents recent research on signal classification and disease diagnosis based
on a third-generation neural network, the spiking neuron network, using medical
data including EEG signals, ECG signals, EMG signals and MRI images. The
advantages and disadvantages of pulsed neural networks compared with
traditional networks are summarized and its development orientation in the
future is prospected
An Energy-Efficient Spiking CNN Implementation for Cross-Patient Epileptic Seizure Detection
This research aims to develop a data-driven computationally efficient strategy for automatic cross-patient seizure detection using spatio temporal features learned from multichannel electroencephalogram (EEG) time-series data. In this approach, we utilize an algorithm that seeks to capture spectral, temporal, and spatial information in order to achieve high generalization. This algorithm's initial step is to convert EEG signals into a series of temporal and multi-spectral pictures. The produced images are then sent into a convolutional neural network (CNN) as inputs. Our convolutional neural network as a deep learning method learns a general spatially irreducible representation of a seizure to improves sensitivity, specificity, and accuracy results comparable to the state-of-the-art results. In this work, in order to avoid the inherent high computational cost of CNNs while benefiting from their superior classification performance, a neuromorphic computing strategy for seizure prediction called spiking CNN is developed from the traditional CNN method, which is motivated by the energy-efficient spiking neural networks (SNNs) of the human brain
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
In this work, we present a neuromorphic system that combines for the first
time a neural recording headstage with a signal-to-spike conversion circuit and
a multi-core spiking neural network (SNN) architecture on the same die for
recording, processing, and detecting High Frequency Oscillations (HFO), which
are biomarkers for the epileptogenic zone. The device was fabricated using a
standard 0.18m CMOS technology node and has a total area of 99mm. We
demonstrate its application to HFO detection in the iEEG recorded from 9
patients with temporal lobe epilepsy who subsequently underwent epilepsy
surgery. The total average power consumption of the chip during the detection
task was 614.3W. We show how the neuromorphic system can reliably detect
HFOs: the system predicts postsurgical seizure outcome with state-of-the-art
accuracy, specificity and sensitivity (78%, 100%, and 33% respectively). This
is the first feasibility study towards identifying relevant features in
intracranial human data in real-time, on-chip, using event-based processors and
spiking neural networks. By providing "neuromorphic intelligence" to neural
recording circuits the approach proposed will pave the way for the development
of systems that can detect HFO areas directly in the operation room and improve
the seizure outcome of epilepsy surgery.Comment: 16 pages. A short video describing the rationale underlying the study
can be viewed on https://youtu.be/NuAA91fdma
Closed-loop approaches for innovative neuroprostheses
The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo
Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing
Reservoir computing (RC) is gaining traction in several signal processing domains, owing to its nonlinear stateful computation, spatiotemporal encoding, and reduced training complexity over recurrent neural networks (RNNs). Previous studies have shown the effectiveness of software-based RCs for a wide spectrum of applications. A parallel body of work indicates that realizing RNN architectures using custom integrated circuits and reconfigurable hardware platforms yields significant improvements in power and latency. In this research, we propose a neuromemristive RC architecture, with doubly twisted toroidal structure, that is validated for biosignal processing applications. We exploit the device mismatch to implement the random weight distributions within the reservoir and propose mixed-signal subthreshold circuits for energy efficiency. A comprehensive analysis is performed to compare the efficiency of the neuromemristive RC architecture in both digital(reconfigurable) and subthreshold mixed-signal realizations. Both EEG and EMG biosignal benchmarks are used for validating the RC designs. The proposed RC architecture demonstrated an accuracy of 90% and 84% for epileptic seizure detection and EMG prosthetic finger control respectively
Integrated Circuits and Systems for Smart Sensory Applications
Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware