Neuromorphic Hardware Accelerator For Heterogeneous Many-Accelerator SoCs


A technical paper titled “SpikeHard: Efficiency-Driven Neuromorphic Hardware for Heterogeneous Systems-on-Chip” was published by researchers at Columbia University. Abstract: "Neuromorphic computing is an emerging field with the potential to offer performance and energy-efficiency gains over traditional machine learning approaches. Most neuromorphic hardware, however, has been designed wi... » read more

3D-Integrated Neuromorphic Hardware With A Two-Level Neuromorphic “Synapse Over Neuron” Structure


A technical paper titled “3D Neuromorphic Hardware with Single Thin-Film Transistor Synapses Over Single Thin-Body Transistor Neurons by Monolithic Vertical Integration” was published by researchers at Korea Advanced Institute of Science and Technology (KAIST) and SK hynix. Abstract: "Neuromorphic hardware with a spiking neural network (SNN) can significantly enhance the energy efficiency... » read more

Analog Circuits Enabling Learning in Mixed-Signal Neuromorphic SNNs, With Tristate Stability and Weight Discretization Circuits


A technical paper titled “Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks” was published by researchers at University of Zurich and ETH Zurich. Abstract: "Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circui... » read more

Analog On-Chip Learning Circuits In Mixed-Signal Neuromorphic SNNs


A technical paper titled "Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks" was published by researchers at Institute of Neuroinformatics, University of Zurich, and ETH Zurich. Abstract: "Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their s... » read more

DW-MTJ Devices For Noise-Resilient Networks For Neuromorphic Computing On The Edge


A technical paper titled "Stochastic domain wall-magnetic tunnel junction artificial neurons for noise-resilient spiking neural networks" was published by researchers at University of Texas at Austin. Abstract: "The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) makes SNNs promising for edge applications that require high energy efficiency. To realize SNNs in har... » read more

Adaptive Memristive Hardware


A new technical paper titled "Self-organization of an inhomogeneous memristive hardware for sequence learning" was just published by researchers at University of Zurich, ETH Zurich, Université Grenoble Alpes, CEA, Leti and Toshiba. "We design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorp... » read more