Related papers: Hybrid Stochastic Synapses Enabled by Scaled Ferro…
Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer far less computational complexity than their biological counterparts. Neurons have dendritic arbors that connect to…
Federated learning on neuromorphic hardware remains unexplored because on-chip spike-timing-dependent plasticity (STDP) produces binary weight updates rather than the floating-point gradients assumed by standard algorithms. We build a…
Realistic physical phenomena exhibit random fluctuations across many scales in the input and output processes. Models of these phenomena require stochastic PDEs. For three-dimensional coupled (vector-valued) stochastic PDEs (SPDEs), for…
Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency. Conventional approaches store synaptic weights in non-volatile memory devices…
Although ferroelectric systems inherently exhibit binary switching behavior, recent advances in analog memory device have spurred growing interest in achieving continuous memory states. In this work, we demonstrate ferroelectric amplitude…
FeFETs hold strong potential for advancing memory and logic technologies, but their inherent randomness arising from both operational cycling and fabrication variability poses significant challenges for accurate and reliable modeling.…
Binarized Neural Networks, a recently discovered class of neural networks with minimal memory requirements and no reliance on multiplication, are a fantastic opportunity for the realization of compact and energy efficient inference…
Deep learning needs high-precision handling of forwarding signals, backpropagating errors, and updating weights. This is inherently required by the learning algorithm since the gradient descent learning rule relies on the chain product of…
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems. However, conventional hardware…
Ferroelectric tunnel junctions (FTJs) leverage polarization-dependent tunneling through ultrathin barriers to enable two-terminal, non-volatile memory and logic. Although conceptually appealing, the practical implementation of conventional…
Ferroelectrics offer a promising materials platform to realize energy-efficient non-volatile memory technology with the FeFET-based implementations being one of the most area-efficient ferroelectric memory architectures. However, the FeFET…
The development of next generation medicines demand more sensitive and reliable label free sensing able to cope with increasing needs of multiplexing and shorter times to results. Field effect transistor-based biosensors emerge as one of…
The use of analog resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and device stochasticity that limit the precision of synapse weights. This challenge can be resolved by emulating analog…
Recent advances in neural networks (NNs) exhibit unprecedented success at transforming large, unstructured data streams into compact higher-level semantic information for tasks such as handwriting recognition, image classification, and…
In the last decade, a 2-terminal passive circuit element called a memristor has been developed for non-volatile resistive random access memory and has more recently shown promise for neuromorphic computing. Compared to flash memory,…
A memory window of ferroelectric field-effect transistors (FeFETs), defined as a separation of the HIGH-state and the LOW-state threshold voltages, is an important measure of the FeFET memory characteristics. In this study, we theoretically…
A probabilistic-bit (p-bit) is the fundamental building block in the circuit network of a stochastic computing, and it could produce a continuous random bit-stream with tunable probability. Utilizing the stochasticity in few-domain…
The primary mechanism of operation of almost all transistors today relies on electric-field effect in a semiconducting channel to tune its conductivity from the conducting 'on'-state to a non-conducting 'off'-state. As transistors continue…
A large effort is devoted to the research of new computing paradigms associated to innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS association. Among various…
Traditional transistors based on complementary metal-oxide-semiconductor (CMOS) and metal-oxide-semiconductor field-effect transistors (MOSFETs) are facing significant limitations as device scaling reaches the limits of Moore's Law. These…