BRN Discussion Ongoing
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TATA have developed what they call a reservoir-based spiking NN:
US2023334300A1 METHODS AND SYSTEMS FOR TIME-SERIES CLASSIFICATION USING RESERVOIR-BASED SPIKING NEURAL NETWORK 20220418
The present disclosure relates to methods and systems for time-series classification using a reservoir-based spiking neural network, that can be used at edge computing applications. Conventional reservoir based SNN techniques addressed either by using non-bio-plausible backpropagation-based mechanisms, or by optimizing the network weight parameters. The present disclosure solves the technical problems of TSC, using a reservoir-based spiking neural network. According to the present disclosure, the time-series data is encoded first using a spiking encoder. Then the spiking reservoir is used to extract the spatio-temporal features for the time-series data. Lastly, the extracted spatio-temporal features of the time-series data is used to train a classifier to obtain the time-series classification model that is used to classify the time-series data in real-time, received from edge devices present at the edge computing network.
To my untutored eye, it does look as if it works on a different principle from Akida.
However, they do use it in some applications in conjunction with a more conventional SNN:
US2024176987A1 METHOD AND SYSTEM OF SPIKING NEURAL NETWORK-BASED ECG 20221125
This disclosure relates generally to method and system for spiking neural network based ECG classifier for wearable edge devices. Employing deep neural networks to extract the features from ECG signal have high computational intensity and large power consumption. The spiking neural network of the present disclosure obtains a training dataset comprising a plurality of ECG time-series data. The spiking neural network comprise a reservoir-based spiking neural network and a feed forward based spiking neural network. Each of the spiking neural network having a logistic regression-based ECG classifier are trained to classify one or more class labels. The peak-based spike encoder of each spiking neural network obtains a plurality of encoded spike trains from the plurality of ECG time-series. The peak-based spike encoder provides high performance for classifying one or more labels. Efficacy of the peak-based spike encoder for classification is experimentally evaluated with different datasets.
[0037] The SNN layer 304 obtains neuronal trace values of a plurality of feed forward neurons from the plurality of encoded spike trains. Further, a second set of spatio-temporal features are extracted based on the neuronal trace values of the plurality of feed forward neurons for each ECG time-series data from each feed-forward neuron.
The patents refer to ''spatio-temporal features" which is a TENNs speciality, but the TATA patents are a bit early for TENNs, but who knows what goes on behind closed doors?
Boab
I wish I could paint like Vincent
From the article. Apparently we may have to wait another couple of years??View attachment 73151
Creating Futuristic Edge Systems with Neuromorphic Computing
As AI techniques on classical processor architecture become a concern, explore neuromorphic computing to create futuristic systems with intelligence at the edge.www.tcs.com
Other processors that are likely to come out within the next couple of years, such as Zeroth, Akida etc., cater to edge applications.
You know son… We’ve been waiting for so many years already… a year or two more won’t exactly break the record! Right?? Greetings from Thailand
Frangipani
Regular
The Fraunhofer HHI Wireless Communications & Networks Team will be presenting a remote live demo of their neuromorphic wireless cognition PoC (the one using a COTS Spot robot dog, developed as a 6G-Research and Innovation Cluster project and funded by the BMBF / German Federal Ministry of Education and Research) at the upcoming IEEE Global Communications Conference in Cape Town, South Africa (8 - 12 December 2024):
Frontgrade Gaisler on LinkedIn: #spacecomputing
We have launched our latest radiation-hardened microcontroller, the GR716B. Building on the success of the GR716A, this latest innovation is a step forward in…
Frontgrade Gaisler Unveils GR716B, a New Standard in Space-Grade Microcontrollers | Frontgrade
Frontgrade Gaisler has launched its latest radiation-hardened microcontroller, the GR716B. Building on the success of the GR716A, this latest innovation is a step forward in the development of energy-efficient and adaptable microcontrollers for space applications.
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The Author must not be aware that BRN is currently in talks on many AKIDA engagements and that there is plenty of current interest in TENN's and PICO.
Tomorrow is within the next couple of years, or, for the pessimists, 1 January 2025.
Humble Genius
Regular
Any link to our Brainchip? Puzzling.....
www.linkedin.com
George Kenwright on LinkedIn: Had a great time consulting on the AI-generated VFX for 'Brainchip', a…
Had a great time consulting on the AI-generated VFX for 'Brainchip', a short film from Asparagus Productions. It's going to be 'mind-blowing'... 🤯 with…
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