Samsung Electronics Baltics’ Post

Implement artificial neural network hardware systems by stacking them like "neuron-synapse-neuron" structural blocks

Implementing artificial neural network hardware systems by stacking them like 'neuron-synapse-neuron' structural blocks https://buff.ly/4bhSMQX

TTO6 has finally closed, and our design is in. The docs for usage and replication of the Verilog module are here: https://lnkd.in/e3tezgHg This was a great demonstration of how bio-plausible neuron models can be realized on a small silicon area for neuromorphic computing.

Will neural networks revolutionize the future of embedded systems? 🧠🤖 In our latest article, we dive deep into the realm of neural networks unveiling the incredible opportunities they open up for the industry. We take a closer look at: ➡️ Implementation challenges and solutions ➡️ Speeding up training and inference ➡️ Leveraging hardware accelerators ➡️ Real-world use cases   With the ongoing debate on how the embedded industry can leverage machine learning, this blog is not to be missed! Read more: https://lnkd.in/dYZ_dABY #NeuralNetworks #MachineLearning #EmbeddedSystems

I am curious what other areas of our lives we will encounter in the future the combination of these two technologies which are neural networks and embedded systems. 🚑👨🏻🔧🚦 It seems to be a very interesting and high-potential development direction.

#Engineers: Join us at the #IMS2024 workshop to learn a convolutional neural network (CNN) for channel estimation using OTA measurements through mmWave PAAM and AMD RFSoC-based 5G NR receiver in a CATR chamber. https://spr.ly/60495BGKB

#Engineers: Join us at the #IMS2024 workshop to learn a convolutional neural network (CNN) for channel estimation using OTA measurements through mmWave PAAM and AMD RFSoC-based 5G NR receiver in a CATR chamber. https://spr.ly/60455IqLx

📅 Day 14 of the #Quantum30 Challenge: 🚀 Quantum Neural Networks with Qiskit 💡 Explored the EstimatorQNN and SamplerQNN classes in a detailed tutorial. NeuralNetwork serves as an abstract base for all QNNs, while EstimatorQNN evaluates quantum observables. SamplerQNN is built on quantum circuit measurement samples obviating the need for a custom observable. #QuantumComputing #QuantumMachineLearning #Quantum30

Spiking Neural Network for LPI Radar Classification: the principle is very interesting, the time frequency image of the radar signal (LFM, Costas, ...) is converted into sipke trains, using LIF neurons according to code rate, this spiking structure interfaces with convolutional layers, max pooling layers and fully connected layers (just like DNN) to give out scores for the different classes (modulation types of the radar signals). It is interesting to note how a continuous time representation, output of the spiking network, interfaces with the features maps of the convolutional layers....