Memristive Accelerators for Data Intensive Computing
... is both no machine
Memristive Accelerators For Data Intensive Computing 4VRahFBRZwg
Engin Ipek, University of Rochester - This tutorial has been part of the Conference on Neuromorphic Materials, Devices, Circuits and Systems (NeuMatDeCas)...
Engin Ipek, University of Rochester - Memristive Accelerators for Data Intensive Computing
Engin Ipek, University of Rochester -
In-memory Computing with Memristors and Memtransistors - Daniele Ielmini
This tutorial has been part of the Conference on Neuromorphic Materials, Devices, Circuits and Systems (NeuMatDeCas) that took ...
Lecture 10 - HW accelerators for learning | Deep Learning on Computational Accelerators
Given by Prof. Avi Mendelson @ CS department of Technion - Israel Institute of Technology.
In-memory computing: Hardware accelerator for embedded AI
In this talk from
Hardware-Aware Quantization for Accurate Memristor-Based Neural Networks | ICCAD 2025 | Dr. S Diware
Hardware-Aware Quantization for Accurate Memristor-Based Neural Networks Presented at ICCAD 2025 🎙️ Speaker: Dr.
Intel® Data Streaming Accelerator | Intel Business
Intel® DSA is a high-performance
Designing an Analog Crossbar based Neuromorphic Accelerator
So you had an advantage over conventional digital
Design for Highly Flexible and Energy-Efficient Deep Neural Network Accelerators [Yu-Hsin Chen]
Abstract: Deep neural networks (DNNs) are the backbone of modern artificial intelligence (AI). While they deliver state-of-the-art ...
Martin Andraud: Accelerating various AI algorithms on the edge: from software to hardware challenges
Abstract: This talk intends to shed light on some hardware/software integration challenges to accelerate (large) AI models on ...
What is In-Memory Computing?
The hardware behind analog AI → http://ibm.biz/analog-AI-hardware Check out the AI hardware toolkit ...
Webinar: Neuromorphic Computing for Edge AI | Fraunhofer IPMS
Neuromorphic
Prof Adnan Mehonic | Memristors for Energy-Efficient Computing Systems | Festival of Research
Prof Adnan Mehonic, Professor of Nanoelectronics and Electronic Materials at UCL EEE, examines how