Loihi Asynchronous Neuromorphic Research Chip

Lines, Andrew; Joshi, Prasad; Liu, Ruokun; McCoy, S.; Tse, Jonathan; Weng, Yi-Hsin; Davies, Mike

doi:10.1109/async.2018.00018

Cited by 31 publications

(17 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First conceptualized during the development of the dynamic vision sensor (DVS), the AER protocol's ternary data format for vision applications is used to encode X-axis and Y-axis coordinates of a pixel and ON or OFF spikes that are generated using a thresholding method to represent luminosity changes [4,57]. Following the successful implementation of AER for neuromorphic vision sensors, the AER protocol has been extended for several other neuromorphic systems, such as tactile [58,59] and auditory sensing [60], along with event-driven processing in neuromorphic hardware implementations [52,61,62].…”

Section: Data-to-event Encoding Using Aeromentioning

confidence: 99%

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Vanarse¹,

Osseiran²,

Rassau

et al. 2022

Sensors

View full text Add to dashboard Cite

Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.

show abstract

Section: Data-to-event Encoding Using Aeromentioning

confidence: 99%

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Vanarse¹,

Osseiran²,

Rassau

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In this section we describe the implementations of the ANN and SNN in standard digital hardware based on how this is done in software on SpiNNaker, the million core neural simulation platform developed at The University of Manchester (Furber et al, 2014 ; Rhodes et al, 2018 ). From the literature describing other implementations, such as the Intel Loihi (Davies et al, 2018 ; Lines et al, 2018 ), we expect these basic steps to be representative of other implementations.…”

Section: Implementations Of the Ann And Snn Versions Of Key Procesmentioning

confidence: 99%

Comparison of Artificial and Spiking Neural Networks on Digital Hardware

Davidson

Furber

2021

Front. Neurosci.

View full text Add to dashboard Cite

Despite the success of Deep Neural Networks—a type of Artificial Neural Network (ANN)—in problem domains such as image recognition and speech processing, the energy and processing demands during both training and deployment are growing at an unsustainable rate in the push for greater accuracy. There is a temptation to look for radical new approaches to these applications, and one such approach is the notion that replacing the abstract neuron used in most deep networks with a more biologically-plausible spiking neuron might lead to savings in both energy and resource cost. The most common spiking networks use rate-coded neurons for which a simple translation from a pre-trained ANN to an equivalent spike-based network (SNN) is readily achievable. But does the spike-based network offer an improvement of energy efficiency over the original deep network? In this work, we consider the digital implementations of the core steps in an ANN and the equivalent steps in a rate-coded spiking neural network. We establish a simple method of assessing the relative advantages of rate-based spike encoding over a conventional ANN model. Assuming identical underlying silicon technology we show that most rate-coded spiking network implementations will not be more energy or resource efficient than the original ANN, concluding that more imaginative uses of spikes are required to displace conventional ANNs as the dominant computing framework for neural computation.

show abstract

“…His statement in the previous section is true for the well-defined dispersionless synaptic delay τ he assumed, but not at all for today's processors, and even less for physically larger computing systems. The recent activity to consider asynchronous operating modes [4,[18][19][20][21] is motivated by admitting that the present synchronized operating mode is disadvantageous in the non-dispersionless world.…”

Section: Scrutinizing Dispersionmentioning

confidence: 99%

Revising the Classic Computing Paradigm and Its Technological Implementations

Végh¹

2021

Informatics

View full text Add to dashboard Cite

Today’s computing is based on the classic paradigm proposed by John von Neumann, three-quarters of a century ago. That paradigm, however, was justified for (the timing relations of) vacuum tubes only. The technological development invalidated the classic paradigm (but not the model!). It led to catastrophic performance losses in computing systems, from the operating gate level to large networks, including the neuromorphic ones. The model is perfect, but the paradigm is applied outside of its range of validity. The classic paradigm is completed here by providing the “procedure” missing from the “First Draft” that enables computing science to work with cases where the transfer time is not negligible apart from the processing time. The paper reviews whether we can describe the implemented computing processes by using the accurate interpretation of the computing model, and whether we can explain the issues experienced in different fields of today’s computing by omitting the wrong omissions. Furthermore, it discusses some of the consequences of improper technological implementations, from shared media to parallelized operation, suggesting ideas on how computing performance could be improved to meet the growing societal demands.

show abstract

Loihi Asynchronous Neuromorphic Research Chip

Cited by 31 publications

References 4 publications

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Comparison of Artificial and Spiking Neural Networks on Digital Hardware

Revising the Classic Computing Paradigm and Its Technological Implementations

Contact Info

Product

Resources

About