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

Recent Developments in Neuromorphic Computing, Focusing on Hardware Design and Reliability

A new technical paper titled "Special Session: Neuromorphic hardware design and reliability from traditional CMOS to emerging technologies" was published by researchers at Univ. Lyon, Ecole Centrale de Lyon, Univ. Grenoble Alpes, Hewlett Packard Labs, CEA-LETI, and Politecnico di Torino. Abstract "The field of neuromorphic computing has been rapidly evolving in recent years, with an incre... » read more

Novel Spintronic Neuro-mimetic Device Emulating the LIF Neuron Dynamics w/High Energy Efficiency & Compact Footprints

New technical paper titled "Noise resilient leaky integrate-and-fire neurons based on multi-domain spintronic devices" from researchers at Purdue University. Abstract "The capability of emulating neural functionalities efficiently in hardware is crucial for building neuromorphic computing systems. While various types of neuro-mimetic devices have been investigated, it remains challenging to... » read more

Neuromorphic Chips & Power Demands

Research paper titled "A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware," from researchers at Graz University of Technology and Intel Labs. Abstract "Spike-based neuromorphic hardware holds the promise to provide more energy efficient implementations of Deep Neural Networks (DNNs) than standard hardware such as GPUs. But this requires to understand how D... » read more

Always-On Sub-Microwatt Spiking Neural Network Based on Spike-Driven Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device

Abstract: "This paper presents a novel spiking neural network (SNN) classifier architecture for enabling always-on artificial intelligent (AI) functions, such as keyword spotting (KWS) and visual wake-up, in ultra-low-power internet-of-things (IoT) devices. Such always-on hardware tends to dominate the power efficiency of an IoT device and therefore it is paramount to minimize its power diss... » read more

2D materials–based homogeneous transistor-memory architecture for neuromorphic hardware

Abstract "In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus exploring homogeneous devices for these components is an important issue for improving module integration and resistance matching. Inspired by ferroelectric proximity effect on two-dimensional materials, we present a tungsten diselenide-on-LiNbO3 cascaded... » read more