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 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

Digital Neuromorphic Processor: Algorithm-HW Co-design (imec / KU Leuven)

A technical paper titled "Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design" was published by researchers at imec and KU Leuven. "In this work, we open the black box of the digital neuromorphic processor for algorithm designers by presenting the neuron processing instruction set and detailed energy consumption of the SENeCA neuromorphic architect... » read more

MEMprop: Gradient-based Learning To Train Fully Memristive SNNs

New technical paper titled "Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays" from IEEE researchers. Abstract "We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to trigger naturally arising voltage spikes. These spikes emitted by memristive dynamics are anal... » read more

Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems

New academic paper from Washington State University, supported by a grant from the National Science Foundation. Abstract: "Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP),... » read more